research on social networks analysis

• Technical Support
• Find My Rep

You are here

The Sage Handbook of Social Network Analysis

• John McLevey - University of Waterloo, Canada
• John Scott - Plymouth University, UK
• Peter J. Carrington - University of Waterloo, Canada
• Description

This new edition of  The   Sage Handbook of Social Network Analysis  builds on the success of its predecessor, offering a comprehensive overview of social network analysis produced by leading international scholars in the field.

Brand new chapters provide both significant updates to topics covered in the first edition, as well as discussing cutting edge topics that have developed since, including new chapters on:

·       General issues such as social categories and computational social science;

·       Applications in contexts such as environmental policy, gender, ethnicity, cognition and social media and digital networks;

·       Concepts and methods such as centrality, blockmodeling, multilevel network analysis, spatial analysis, data collection, and beyond.

By providing authoritative accounts of the history, theories and methodology of various disciplines and topics, the second edition of  The SAGE Handbook of Social Network Analysis  is designed to provide a state-of-the-art presentation of classic and contemporary views, and to lay the foundations for the further development of the area.

PART 1: GENERAL ISSUES

PART 2: APPLICATIONS

PART 3: CONCEPTS AND METHODS

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

Preview this book

Select a purchasing option.

This title is also available on SAGE Knowledge , the ultimate social sciences online library. If your library doesn’t have access, ask your librarian to start a trial .

This title is also available on SAGE Research Methods , the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial .

• Contact Us: [email protected]

Social Network Analysis 101: Ultimate Guide

Comprehensive introduction for beginners.

Social network analysis is a powerful tool for visualizing, understanding, and harnessing the power of networks and relationships. At Visible Network Labs, we use our network science and mapping tools and expertise to track collaborative ecosystems and strengthen systems change initiatives. In this Comprehensive Guide, we’ll introduce key principles, theories, terms, and tools for practitioners framed around social impact, systems change, and community health improvement. Let’s dig in!

Learn more and get started with the tools below in our complete Guide.

Table of Contents

You can read this guide from start-to-finish or use the table of contents to fast forward to a topic or section of interest to you. The guide is yours to use as you see fit.

Introduction

Let’s start by reviewing the basics, like a definition, why SNA is important, and the history of the practice. If you want a quick intro to this methodology, download our Social Network Analysis Brief .

Definition of Social Network Analysis (SNA)

Social Network Analysis , or SNA, is a research method used to visualize and analyze relationships and connections between entities or individuals within a network. Imagine mapping the relationships between different departments in a corporation. The outcome would be a vivid picture of how each department interacts with others, allowing us to see communication patterns, influential entities, and bottlenecks

The Importance of SNA

SNA is a powerful tool. It allows us to explore the underlying structure of an organization or network, identifying the formal and informal relationships that drive the formal processes and outcomes. This insight can enable better communication, facilitate change management, and inspire more efficient collaboration.

This methodology also helps demonstrate the impact of relationship-building and systems change efforts by documenting the changes in the quality and quantity of relationships before and after the initiative. The maps and visualizations produced by SNA are an engaging way to share your progress and impact with stakeholders, donors, and the community at large.

Brief Historical Overview of SNA

The concept of SNA emerged in the 1930s within the field of sociology. Its roots, however, trace back to graph theory in mathematics. It was not until the advent of computers and digital data in the 1980s and 1990s that SNA became widely used, revealing new insights about organizational dynamics, community structures, and social phenomena.

While it originated as an academic research tool, it is increasingly used to inform real-world practice. Today, it is used in a broad variety of industries, fields, and sectors, including business, web development, public health, foundations and philanthropy , telecommunications, law enforcement, academia, and systems change initiatives, to name a few.

Fundamentals of SNA

SNA is a broad topic, but these are some of the essential terms, concepts, and theories you need to know to understand how it works.

Nodes and Edges

In SNA, nodes represent individuals or entities while edges symbolize the relationships between them. For example, in an inter-organizational network, nodes might be companies, and edges could represent communication, collaboration, or competition.

Network Types

Different types of networks serve different purposes. ‘Ego Networks’ focus on one node and its direct connections, revealing its immediate network. ‘Whole Networks’, on the other hand, capture a broader picture, encompassing an entire organization or system. Open networks are loosely connected, with many opportunities to build new connections, ideal for innovation and idea generation – while closed networks are densely interconnected, better for refining ideas amongst a group who all know each other.

Network Properties

Properties such as density (the proportion of potential connections that are actual connections), diameter (the longest distance between two nodes), and centrality (the importance of a node within the network) allow us to understand the network’s structure and function. Metrics also can measure relationship quality across the network, like our validated trust and value scores.

Dyadic and Triadic Relationships

Dyadic relationships involve two nodes, like a partnership between two companies. Triadic relationships, involving three nodes, are more complex but can offer richer insights. For instance, it might show how a third company influences the relationship between two others, or which members of your network are the best at building new relationships between their peers.

Homophily and Heterophily

Homophily refers to the tendency of similar nodes to connect, while heterophily is the opposite. In a business context, we might see homophily between companies in the same industry and heterophily when seeking diversity in a supply chain. Many networks aim to be diverse but get stuck talking to the same, similar partners. These network concepts underly many strategies promoting network innovation to avoid group-think among likeminded partners.

Network Topologies

Lastly, the layout or pattern of a network, its topology, can reveal much about its function. For instance, a centralized topology, where one node is connected to all others, may indicate a hierarchical organization, while a decentralized topology suggests a more collaborative and flexible environment. This is also referred to as the structure of the network. Read more.

Theoretical Background of SNA

Many different theories have developed to explain how certain network properties, like their topology, centrality, or type, lead to different outcomes. Here are several key theories relevant to SNA.

Strength of Weak Ties Theory

This theory postulates that weak ties or connections often provide more novel information and resources compared to strong ties. These “weak” relationships, which may seem less important, can serve as important bridges between different clusters within a network. Read more.

Structural Hole Theory

This theory posits that individuals who span the structural holes, or gaps, in a network—acting as a bridge between different groups—hold a strategic advantage. They can control and manipulate information and resources flowing between the groups, making their position more influential. Read more

Small World Network Theory

This theory emphasizes the interconnectedness of nodes within a network. It suggests that most nodes can be reached from any other node through a relatively short path of connections. This property leads to the famous phenomenon of “six degrees of separation,” indicating efficient information transfer and connectivity in a network.

Barabási–Albert (Scale-Free Network) Model

This model suggests that networks evolve over time through the process of preferential attachment, where new nodes are more likely to connect to already well-connected nodes. This results in “scale-free” networks, where a few nodes (“hubs”) have many connections while the majority of nodes have few.

Data Collection and Preparation

Every network mapping begins by collecting and preparing data before it can be analyzed. This data varies widely, but at a basic level, they must include data on nodes (the entities in the network) and data on edges (the lines between nodes representing a relationship or connection). Additional data on the attributes of the nodes or edges add more levels of analysis and insight but are not strictly necessary.

Primary Methods for Collecting SNA Data

This can be as simple as conducting interviews or surveys within an organization. The more complex the network, the more difficult it is to collect good primary data: If you have more than 5-10 partners, interviews and surveys are hard to conduct by hand.

Network survey tools like PARTNER collect relational data by asking respondents who they are connected to, and then asking them about aspects of their relationships to provide trust, value, and network structure scores. This is impossible to do using most survey software like Google Forms without hours of cleaning by hand.

Response rates are an important consideration if using surveys for data collection. Unlike a typical survey where a small sample is representative, a network survey requires a high response rate – 80% and above are considered the gold standard.

In an inter-organizational context where surveys are impossible, or you cannot achieve a valid response rate, one might gather data through business reports, contracts, or publicly available data on partnerships and affiliations. For example, you could visit an organization’s website to note who they list as a partner – and do the same for others – to generate a basic SNA map.

Secondary Sources of SNA Data

Secondary sources include data that was already collected but can be used again, often to complement your use of primary data you collect yourself. This might include academic databases, industry reports, or social media data. It’s important to ensure the accuracy and reliability of these sources.

You can also conduct interviews or focus groups with network members to add a qualitative perspective to your results. These mixed-method SNA projects provide a great deal more depth to their network maps through their conversations with numerous network representatives to explore deeper themes and perspectives.

Ethical Considerations in Data Collection

When collecting data, it’s crucial to ensure privacy, obtain necessary permissions, and anonymize data where necessary. Respecting these ethical boundaries is critical for maintaining trust and integrity in your work.

Consider also how your SNA results will be used. For example, network analysis can help assess how isolated an individual is to target them for interventions. Still, it could also be abused by insurance companies to charge these individuals a higher rate (loneliness increases your risk of death).

Lastly, consider ways to involve the communities with stake in your SNA using approaches like community-based participatory research. Bring in representatives from target populations to help co-design your initiative or innovation as partners, rather than patients or research subjects.

Preparing Data for Analysis

Data needs to be formatted correctly for analysis, often as adjacency matrices or edgelists. Depending on the size and complexity of your network, this can be a complex process but is crucial for meaningful analysis.

If you are new to SNA, you can start by laying out your data in tables. For example, the table below shows a relational data set for a set of partners within a public health coalition. The first column shows the survey respondent (Partner 1), the second shows who they reported as a partner, the third shows their reported level of trust, and the fourth their reported level of collaboration intensity. This is just one of many ways to lay out and organize network data.

Depending on which analysis tool you choose, a varying degree of data preparation and cleaning will be required. Usually, free tools require the most work, while software with subscriptions do a lot of it for you.

Network Analysis Methods & Techniques

There are many ways to analyze a network or set of entities using SNA. Here are some of basic and advanced techniques, along with info on network visualization – a major component and common output of SNA projects.

Basic Technique: Network Centrality

One of the most common ways to analyze a network is to look at the centrality of various nodes to identify key players, information hubs, and gatekeepers across the network. There are three types of centrality, each corresponding to a different aspect of connectivity and centrality. Degree, Betweenness, and Closeness Centrality are measures of a node’s importance.

Degree Centrality  

Can be used to identify the most connected actors in the network. These actors are considered “popular” or “active” and they often have a strong influence within the network due to their numerous direct connections. In a coalition or network, these nodes could be the organizations or individuals that are most active in participating or the most engaged in the network activities. They may be the ‘go-to’ people for information or resources and have a significant impact on shaping the group’s agenda.

Betweenness Centrality

A useful for identifying the “brokers” or “gatekeepers” in the network. These actors have a unique position where they connect different parts of the network, facilitating or controlling the flow of information between others. In a coalition context, these could be the organizations or individuals who have influence over how information, resources, or support flow within the network, by virtue of their position between other key actors. These actors could play crucial roles in collaboration, negotiation, and conflict resolution within the network.

Closeness Centrality

A measure of how quickly a node can reach every other node in the network via the shortest paths. In a coalition, these nodes can disseminate information or exert influence quickly due to their close proximity to all other nodes. These ‘efficient connectors’ are beneficial for the rapid spread of information, resources, or innovations across the network. They could play a vital role during times of rapid change or when swift collective action is required.

Advanced Techniques: Clusters and Equivalence

Clustering Coefficients

The Clustering Coefficient provides insights into the “cliquishness” or local cohesion of the network around specific nodes. In a coalition or inter-organizational network, a high clustering coefficient may indicate that a node’s connections are also directly connected to each other, forming tight-knit groups or sub-communities within the larger network. These groups often share common interests or objectives, and they might collaborate or share resources more intensively. Understanding these clusters can be crucial for coalition management as it can highlight potential subgroups that may need to be engaged differently, or that might possess different levels of influence or commitment to the coalition’s overarching goals.

Structural Equivalence

Structural Equivalence is used to identify nodes that have similar patterns of connections, even if they do not share a direct link. In a coalition context, structurally equivalent organizations or individuals often occupy similar roles or positions within the network, and thus may have similar interests, influence, or responsibilities. They may be competing or collaborating entities within the same sectors or areas of work. Understanding structural equivalence can provide insights into the dynamics of the network, such as potential redundancies, competition, or opportunities for collaboration. It can also reveal how changes in one part of the network may impact other, structurally equivalent parts of the network.

Visualizing Networks

Network visualization is a key tool in Social Network Analysis (SNA) that allows researchers and stakeholders to see the ‘big picture’ of the network structure, as well as discern patterns and details that may not be immediately evident from numerical data. Here are some key aspects and benefits of network visualization in the context of a coalition or inter-organizational network:

Overview of Network Structure: Visualizations provide a snapshot of the entire network structure, including nodes (individuals or organizations) and edges (relationships or interactions). This helps to comprehend the overall size, density, and complexity of the network. Seeing these relationships mapped out can often make the network’s structure more tangible and easier to understand.

Identification of Key Actors: Centrality measures can be represented visually, making it easier to identify key actors or organizations within the network. High degree nodes, gatekeepers, and efficient connectors will stand out visually, which can assist in identifying who holds influence or power within the network.

Detecting Subgroups and Communities: Visualization can also highlight clusters or subgroups within the network. These might be based on shared interests, common goals, or frequent interaction. Understanding these subgroups is crucial for coalition management and strategic planning, as different groups might have unique needs, concerns, or levels of engagement.

Identifying Outliers and Peripheral Nodes: Network visualizations can also help in identifying outliers or peripheral nodes – those who are less engaged or connected within the network. These actors might represent opportunities for further engagement or potential risks for network cohesion.

Highlighting Network Dynamics: Visualizations can be used to show changes in the network over time, such as the formation or dissolution of ties, the entry or exit of nodes, or changes in nodes’ centrality. These dynamics can provide valuable insights into the evolution of the coalition or network and the impact of various interventions or events.

Software and Tools for SNA

SNA software helps you collect, clean, analyze, and visualize network data to simplify the process of of analyzing social networks. Some tools are free with limited functionality and support, while others require a subscription but are easier to use and come with support. Here are some popular s tools used across many application

Introduction to Popular SNA Tools

Tools like UCINet, Gephi, and Pajek are popular for SNA. They offer a variety of functions for analyzing and visualizing networks, accommodating users of varying skill levels. Here are ten tools for use in different contexts and applications.

• UCINet: A comprehensive software package for the analysis of social network data as well as other 1-mode and 2-mode data.
• NetDraw: A tool usually used in tandem with UCINet to visualize networks.
• Gephi: An open-source network analysis and visualization software package written in Java.
• NodeXL: A free and open-source network analysis and visualization software package for Microsoft Excel.
• Kumu: A powerful visualization platform for mapping systems and better understanding relationships.
• Pajek: Software for analysis and visualization of large networks, it’s particularly good for handling large network datasets.
• SocNetV (Social Networks Visualizer): A user-friendly, free and open-source tool.
• Cytoscape: A bioinformatics software platform for visualizing molecular interaction networks.
• Graph-tool: An efficient Python module for manipulation and statistical analysis of graphs.
• Polinode: Tools for network analysis, both for analyzing your own network data and for collecting new network data.

Choosing the Right Tool for Your Analysis:

The right tool depends on your needs. For beginners, a user-friendly interface might be a priority, while experienced analysts may prefer more advanced functions. The size and complexity of your network, as well as your budget, are also important considerations.

PARTNER CPRM: A Community Partner Relationship Management System for Network Mapping

For example, we created PARTNER CPRM, a Community Partner Relationship Management System, to replace the CRMs used by most organizations to manage their relationships with their network of strategic partners. Incorporating data collecting, analysis, and visualization features alongside CRM tools like contact management and email tracking, the result is a powerful and easy-to-use network mapping tool.

SNA Case Studies

Looking for a real-world example of a social network analysis project? Here are three examples from recent projects here at Visible Network Labs.

Case Study 1: Leveraging SNA for Program Evaluation

SNA is increasingly becoming a vital tool for program evaluation across various sectors including public health, psychology, early childhood, education, and philanthropy. Its potency is particularly pronounced in initiatives centered around network-building.

Take for instance the Networks for School Improvement Portfolio by the Gates Foundation. The Foundation employed PARTNER, an SNA tool, to assess the growth and development of their educator communities over time. The SNA revealed robust networks that offer valuable benefits to members by fostering information exchange and relationship development. By repeating the SNA process at different stages, they could verify their ongoing success and evaluate the effectiveness of their actions and adjustments.

Read the Complete Case Study Here

Case Study 2: Empowering Coalition-building

In the realm of policy change, building a coalition of partners who share a common goal can be pivotal in overturning the status quo. SNA serves as a strategic tool for developing a coalition structure and optimizing pre-existing relationships among the members.

The Fix CRUS Coalition in Colorado, formulated in response to the closure of five major peaks to public access, is a prime example of this. With the aim of strengthening state liability protections for landowners, the coalition employed PARTNER to evaluate their network and identify key players. Their future plans involve mapping connections to important legislators as their bill progresses through the state legislature. Additionally, their network maps and reports will prove instrumental in acquiring grants and funding.

Case Study 3: Boosting Employee Engagement

In the private sector, businesses are increasingly harnessing SNA to optimize their employee networks, both formal and informal, with the goal of enhancing engagement, productivity, and morale.

Consider the case of Acuity Insurance. In response to a transition to a Hybrid-model amid the COVID-19 pandemic, the company started using PARTNER to gather network data from their employees. Their aim was to maintain their organizational culture and keep employee engagement intact despite the model change. Their ongoing SNA will reveal the level of connectedness within their team, identify employees who are over-networked (and hence at risk of burnout), and pinpoint those who are under-networked and could be missing crucial information or opportunities.

Read More About the Project Here

Challenges and Future Directions in Network Analysis

Like all fields and practices, social network analysis faces certain limitations. Practitioners are constantly innovating to find better ways to conduct projects. Here are some barriers in the field and current trends and predictions about the future of SNA.

The Limitations of SNA

SNA is a powerful tool, but it’s not without limitations. It can be time-consuming and complex, particularly with larger networks. Response rates are important to ensure accuracy, which makes data collection more difficult and time-consuming. SNA also requires quality, validated data, and the interpretation of results can be subjective. Software that helps to address these problems requires a significant investment, but the results are often worth it.

Lastly, SNA is a skill that takes time and effort to learn. If you do not have someone in-house with network analysis skills, you may need to hire someone to carry out the analysis or spend time training an employee to build the capacity internally.

Current Trends and Future Predictions

One emerging trend is the increased application of SNA in mapping inter-organizational networks such as strategic partnerships, community health ecosystems, or policy change coalitions. Organizations are realizing the power of these networks and using SNA to navigate them more strategically. With SNA, they can identify key players, assess the strength of relationships, and strategize on how to optimize their network for maximum benefit.

In line with the rise of data science, another trend is the integration of advanced analytics and machine learning with SNA. This fusion allows for the prediction of network behaviors, identification of influential nodes, and discovery of previously unnoticed patterns, significantly boosting the value derived from network data.

The future of SNA is likely to see a greater emphasis on dynamic networks – those that change and evolve over time. With increasingly sophisticated tools and methods, analysts will be better equipped to track network changes and adapt strategies accordingly.

In addition, there is a growing focus on inter-organizational network resilience. As global challenges such as pandemics and climate change underscore the need for collaborative solutions, understanding how these networks can withstand shocks and adapt becomes crucial. SNA will play an instrumental role in identifying weak spots and strengthening the resilience of these networks.

Conclusion: Social Network Analysis 101

SNA offers a unique way to visualize and analyze relationships within a network, be it within an organization or between organizations. It provides valuable insights that can enhance communication, improve efficiency, and inform strategic decisions.

This guide provides an overview of SNA, but there is much more to learn. Whether you’re interested in the theoretical underpinnings, advanced techniques, or the latest developments, we encourage you to delve deeper into this fascinating field.

Resources and Further Reading

For those who want to build more SNA skills and learn more about network science, check out these recommendations for further reading and exploration from the Visible Network Labs team of network science experts.

Recommended Books on SNA

• “Network Science” by Albert-László Barabási – A comprehensive introduction to the theory and applications of network science from a leading expert in the field.
• “Analyzing Social Networks” by Steve Borgatti, Martin Everett, and Jeffrey Johnson – An accessible introduction, complete with software instructions for carrying out analyses.
• “Social Network Analysis: Methods and Applications” by Stanley Wasserman and Katherine Faust – A more advanced, methodological book for those interested in a deep dive into the methods of SNA.
• “Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives” by Nicholas Christakis and James Fowler – An engaging exploration of how social networks influence everything from our health to our political views.
• “The Network Imperative: How to Survive and Grow in the Age of Digital Business Models” by Barry Libert, Megan Beck, and Jerry Wind – An excellent book for those interested in applying network science in a business context.
• “Networks, Crowds, and Markets: Reasoning About a Highly Connected World” by David Easley and Jon Kleinberg – An interdisciplinary approach to understanding networks in social and economic systems. This book combines graph theory, game theory, and market models.

Online Resources and Courses

Here are some online learning opportunities, including online courses, communities, resources hubs, and other places to learn about social network analysis.

• Social Network Analysis  by Lada Adamic from the University of Michigan
• Social and Economic Networks: Models and Analysis  by Matthew O. Jackson from Stanford University
• Introduction to Social Network Analysis  by Dr. Jennifer Golbeck from the University of Maryland, College Park
• Statistics.com :   Statistics.com offers a free online course called  Introduction to SNA  taught by Dr. Jennifer Golbeck.
• The Social Network Analysis Network:  This website provides a directory of resources on network methods, including courses, books, articles, and software.
• The SNA Society:  This organization provides a forum for social network analysts to share ideas and collaborate on research. They also offer a number of resources on their website, including a list of online courses.

Journals and Research Papers on SNA

These are a few of the most influential cornerstone research papers in network science and analysis methods:

• “The Strength of Weak Ties” by Mark Granovetter (1973)
• “Structural Holes and Good Ideas” by Ronald Burt (2004)
• “ Collective dynamics of ‘small-world’ networks” by Duncan Watts & Steven Strogatz (1998)
• “The structure and function of complex networks.” by M.E. Newman (2003).
• “Emergence of scaling in random networks” by A. Barabasi (1999).

Check out these peer-reviewed journals for lots of network science content and information:

• Social Networks : This is an interdisciplinary and international quarterly journal dedicated to the development and application of network analysis.
• Network Science : A cross-disciplinary journal providing a unified platform for both theorists and practitioners working on network-centric problems.
• Journal of Social Structure (JoSS) : An electronic journal dedicated to the publication of network analysis research and theory.
• Connections : Published by the International Network for Social Network Analysis (INSNA), this journal covers a wide range of social network topics.
• Journal of Complex Networks : This journal covers theoretical and computational aspects of complex networks across diverse fields, including sociology.

Frequently Asked Questions about SNA

A: SNA is a research method used to visualize and analyze relationships and connections within a network. In an organizational context, SNA can be used to explore the structure and dynamics of an organization, such as the informal connections that drive formal processes. It can reveal patterns of communication, identify influential entities, and detect potential bottlenecks or gaps.

A: The primary purpose of SNA is to uncover and visualize the relationships between entities within a network. By doing so, it allows us to understand the network’s structure and dynamics. This insight can inform strategic decision-making, facilitate change management, and enhance overall efficiency within an organization.

A: SNA allows researchers to examine the relationships between entities, the overall structure of the network, and the roles and importance of individual entities within it. This can involve studying patterns of communication, collaboration, competition, or any other type of relationship that exists within the network.

A: SNA has a wide range of applications across various fields. In business, it’s used to analyze organizational structures, supply chains, and market dynamics. In public health, it can map the spread of diseases. In sociology and anthropology, SNA is used to study social structures and relationships. Online, SNA is used to study social media dynamics and digital marketing strategies.

A: Key concepts in SNA include nodes (entities) and edges (relationships), network properties like density and centrality, and theories such as the Strength of Weak Ties and Structural Hole Theory. It also encompasses concepts like homophily and heterophily, which describe the tendency for similar or dissimilar nodes to connect.

A: An example of SNA could be a study of communication within a corporation. By treating departments as nodes and communication channels as edges, analysts could visualize the communication network, identify key players, detect potential bottlenecks, and suggest improvements.

A: Social Network Analysis refers to the method of studying the relationships and interactions between entities within a network. It involves mapping out these relationships and applying various analytical techniques to understand the structure, dynamics, and implications of the network.

A: In psychology, SNA can be used to study the social relationships between individuals or groups. It might be used to understand the spread of information, the formation of social groups, the dynamics of social influence, or the impact of social networks on individual behavior and well-being.

A: SNA can be conducted at different levels, depending on the focus of the study. The individual level focuses on a single node and its direct connections (ego networks). The dyadic level looks at the relationship between pairs of nodes, while the triadic level involves three nodes. The global level (whole network) considers the entire network.

A: There are several types of networks in SNA, including ego networks (focused on a single node), dyadic and triadic networks (focused on pairs or trios of nodes), and whole networks. Networks can also be categorized by their structure (like centralized or decentralized), by the type of relationships they represent, or by their application domain (such as organizational, social, or online networks).

A: SNA is used to visualize and analyze the relationships within a network. Its insights can inform strategic decisions, identify influential entities, detect potential weaknesses or vulnerabilities, and enhance the efficiency of communication or processes within an organization or system. It’s also an essential tool for research in fields like sociology, anthropology, business, public health, and digital marketing.

Connect with our Team!

Contact the VNL team to demo PARTNER™ or discuss a research or evaluation project. We can help you learn more about our services, help brainstorm project designs, and provide a custom scope based on your budget and needs. We look forward to connecting! 

Email our team: [email protected]

Send a message: Contact Us Here

Get Involved!

Let us know how you’d like to get involved with the Jeffco PARTNER CPRM by choosing from the options below.

Join our next webinar: Marketing & Communication Strategies & Tactics for Networks & Coalitions

Choose a free gift.

Click one of the links below to download a free resource to strengthen your community partnerships, collaborative network, and strategic ecosystem. 

Network Leadership Guide

Advice for building, managing, and assessing cross-sector networks or coalitions of partners.

Ecosystem Mapping Template

A template to map the connections and interactions between key stakeholders in your community.

Network Strategy Planner

A worksheet and guide to help you think through and develop your network or ecosystem strategy.

Subscribe to our Network Science Newsletter!

Get monthly updates on VNL news, new research, funding opportunities, and other resources related to network and ecosystem mapping and management.

New Developments in Social Network Analysis

This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?” Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 9 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

• Related Documents

An Exploratory Analysis Using Co-Authorship Network

Social network analysis has been widely used by organizational behavior researchers to stress the importance of the context, social connections, and social structure on human behavior. In the last decade, social network analysis has emerged as one of the most useful techniques for exploring online social networks, world wide web, e-mail traffic, and logistic operations. In this chapter, the authors present an application of social network analysis techniques for academic research. The authors choose Kahneman and Tversky's prospect theory as the focus of their analysis and, based on that, develop a co-authorship structure that depicts in a clear manner the key authors and/or the researchers that dominate and bridge different sub-fields in the field of management. The authors discuss the implications of this study for academic research and management discipline.

Analysis of Online Social Networks for the Identification of Sarcasm

With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved for communication amongst humans. OSNs have given us the opportunity to monitor and mine the opinions of a large number of online active populations in real time. Many diverse approaches have been proposed, various datasets have been generated, but there is a need of collective understanding of this area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data generated via online social networks. It argues and discusses various techniques and solutions available in literature currently. In the end, the chapter provides some answers to the open-ended question and future research directions related to the analysis of textual data.

Introduction

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Social Network Analysis Visualization

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

The Mathematics of Social Network Analysis: Metrics for Academic Social Networks

Predicting violent victimization using social network analysis from police data.

Extant research suggests that membership in crime networks explains vulnerability to violent crime victimization. Consequently, identifying deviant social networks and understanding their structure and individual members' role in them could provide insight into victimization risk. Identifying social networks may help tailor crime prevention strategies to mitigate victimization risks and dismantle deviant networks. Social network analysis (SNA) offers a particular means of comprehending and measuring such group-level structures and the roles that individuals play within them. When applied to research on crime and victimization, it could provide a foundation for developing precise, effective prevention, intervention, and suppression strategies. This study uses police data to examine whether individuals most central to a deviant social network are those who are most likely to become victims of violent crime, and which crime network roles are most likely to be associated with vulnerability to violent victimization. SNA of these data indicates that network individuals who are in a position to manage the flow of information in the network (betweenness centrality), regardless of their number of connections (degree centrality), are significantly more likely to be homicide and aggravated assault victims. Implications for police practice are discussed.

Entanglement, Materiality and the Social Organisation of Construction Workers in Classical Athens

This chapter views the “Periclean Building Program” through the lens of Actor Network Theory, in order to explore the ways in which the construction of these buildings transformed Athenian society and politics in the fifth century BC. It begins by applying some Actor Network Theory concepts to the process that was involved in getting approval for the building program as described by Thucydides and Plutarch in his Life of Pericles. Actor Network Theory blends entanglement (human-material thing interdependence) with network thinking, so it allows us to reframe our views to include social networks when we think about the political debate and social tensions in Athens that arose from Pericles’s proposal to construct the Parthenon and Propylaea on the Athenian Acropolis, the Telesterion at Eleusis, the Odeon at the base of the South slope of the Acropolis, and the long wall to Peiraeus. Social Network Analysis can model the social networks, and the clusters within them, that existed in mid-fifth century Athens. By using Social Network Analysis we can then show how the construction work itself transformed a fractious city into a harmonious one through sustained, collective efforts that engaged large numbers of lower class citizens, all responding to each other’s needs in a chaine operatoire..

Local Community Extraction in Social Network Analysis

To identify global community structures in networks is a great challenge that requires complete information of graphs, which is infeasible for some large networks, e.g. large social networks. Recently, local algorithms have been proposed to extract communities for social networks in nearly linear time, which only require a small part of the graphs. In local community extraction, the community extracting assignments are only done for a certain subset of vertices, i.e., identifying one community at a time. Typically, local community detecting techniques randomly start from a vertex and gradually merge neighboring vertices one-at-a-time by optimizing a measure metric. In this chapter, plenty of popular methods are presented that are designed to obtain a local community for a given graph.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Export Citation Format

Share document.

Skip to content

Read the latest news stories about Mailman faculty, research, and events. 

Departments

We integrate an innovative skills-based curriculum, research collaborations, and hands-on field experience to prepare students.

Learn more about our research centers, which focus on critical issues in public health.

Our Faculty

Meet the faculty of the Mailman School of Public Health. 

Become a Student

Life and community, how to apply.

Learn how to apply to the Mailman School of Public Health. 

Social Network Analysis

Social Network analysis is the study of structure, and how it influences health, and it is based on theoretical constructs of sociology and mathematical foundations of graph theory. Structure refers to the regularities in the patterning of relationships among individuals, groups and/or organizations. When social network analysis is undertaken, the underlying assumption is that network structure, and the properties of that structure have significant implications on the outcome of interest.

Due to its focus on network structure rather than individual characteristics and or behaviors of network members, the data required for appropriate analysis differs from what is typically collected in non-relational epidemiologic study designs. Typically, study designs that focus on individual characteristics/behaviors and how those characteristics influence health, collect and conduct analysis on attribute data. Attribute data is defined as data that reflects the attitudes, opinions, and behaviors of individuals or groups. Conversely, social network analysis requires not only attribute data, but is built on the collection and analysis of relational data. Relational data refers to contacts, ties and connections, which relate one agent in a network to another. Relational data cannot be reduced to properties of the individual agents themselves but to a system/collection of agents.

Description

The majority of social network studies use either whole (Socio-centric) networks or egocentric study designs. Whole network studies assess relationships between individuals or actors that for analytical purposes are regarded as bounded or closed, even though in actuality the boundaries of the network are in fact permeable and/or ambiguous. When whole network studies are conducted, the focus of the study is to measure the structural patterns of how individuals within the network interact and how those patterns explain specific health outcomes. The underlying assumption made when whole network analysis is conducted, is that individuals that make up a group or social network will interact more than would a randomly selected group of similar size.

In a socio-centric study, members of the network are usually known or are easily determined because the focus is usually on closed networks that are a priori defined. For this reason, data collection for socio-centric network analysis involves enumerating all network members, and administering saturation surveys to all network members. A saturation survey provides respondents with a roster of all network members, and respondents are asked to identify members with whom they are affiliated. From this data, actor-by-actor matrices can be constructed and social network analysis can be conducted.

When the network of interest does not have clearly defined boundaries, socio-centric studies result in snowball or respondent driven sampling to generate the network and collect data to identify structural patterns. In respondent driven sampling, a small number of network members are interviewed and asked to name other network members, and those named members are also interviewed and asked to name other network members. This iterative process is continued until all network members are identified, or for an a priori set number of waves established before study initiation. The assumption made when respondent driven sampling is used is that the sampled network is representative of all other segments of the network from which data has not been collected. Respondent driven sampling uses name generator surveys to identify network members, followed by name interpreter questions to solicit information about the named actors, their characteristics, and relations to the focal actors.

Egocentric network designs, on the other hand, focus on a focal actor, ego, and the relationships between the ego and named actors or objects within their social networks. These types of designs collect data on the relationships involving the ego and the objects, alters, to which they are linked. Egocentric study designs use either name generators or position generators to obtain both attribute and relational data that can be used to construct actor-by-actor from which egocentric data analysis can be constructed. Position generators are used to identify people who fill particular value rolls, such as lawyers, where as name generators, as discussed above, are questionnaires that ask the ego questions about individuals to whom he or she is connected in a specific way. Unlike in socio-centric studies, however, resource constraints preclude the subsequent interview of named alters, and therefore the ego serves as the informant for not only their own relationships with the alters, but also the alters relationships with each other. Name generator questions like in socio-centric respondent driven sampling are usually followed by name interpreter questionnaires.

Analysis of Social Network Data

Network data, though collected at the level of the individual, is analyzed at the structural level. Data is organized as an actor-by-actor matrix as depicted in figure 1B. Data as displayed in figure one depicts the presence or absence of a tie. When the strength of a tie is also of interest, i.e. valued data, similarity or distance matrices could be used. Similarity matrices depict stronger ties with increasing numerical values, while increasing numerical values in distance matrices reflect weakened ties because the greater the distance between two actors, the weaker the ties. Any actor-by actor matrix can be converted into graphs and analyzed using social network analysis software such as UCINET. Graphs are visual representations of a network. Actors within a network are displayed as nodes and the lines connecting nodes are representative of the ties between two actors. Graphs can be directed, indicating the relationship is directed from one agent to the other, or valued, indicating the strength of the tie. Though, visualizing the data is informative, the crux of social network analysis lies in the calculation of descriptive measures that reveal important characteristics about 1) position of network actors, 2) properties of network subgroups, and 3) characteristics of complete networks.

Position of network actors or the interconnectedness of network actors is often referred to as a measure of cohesion. There are two common measures of cohesion

Distance= the length of the shortest path that connects two actors

(Howe et al.) Distance between points 15 and 11 is 5

Density = total number of relational ties divided by the total possible number of relationional ties

Components and cliques measure properties of network subgroups

A component is a portion of the network in which all actors are connected, either directly or indirectly.

(Howe et al.)

Nodes 1, 6, and & 7 form a clique

A clique is a subgroup of actors who are all directly connected to one another, and no other member of the network is connected to all members of the subgroup. Clique analysis is the most common techniques used to identify dense subgroups within a network. Characteristics of complete networks are defined in terms of centrality. Centrality measures identify the most prominent actors within a network. It can be conceptualized as either local or global. Local centrality refers to the direct ties a particular node has, while global centrality refers to the number of direct and indirect ties of a particular node. Centrality is measured in terms of betweenness or degree. Betweenness refers to the number of times an actor connects different subgroups of a network that would otherwise not be connected. In figure 3 above, node 19 connects nodes 13, 8, 17, 12, 14, and 15 to the main network and serves as a prominent actor within the network. Its prominence is reiterated when degree centrality is considered. Degree centrality refers to the sum of all actors that are directly connected to an ego.

Node number 19 has a degree centrality of 9, which is the highest in the sociograph. The overall centralization measure refers to how tightly a graph is organized around its most central point. The measures of network structure that have been discussed above can then be use to parameterize predictive regression models that relate relational data to attribute data. For example, after generating measures of network structure using social network analysis methods, Lee et al used multivariable regression to evaluate associations between centrality measures and hospital characteristics.

Textbooks & Chapters

Scott J. Social network analysis: a handbook. Newbury Park: Sage, 2000. This book provides an introduction to social network analysis. It briefly reviews the theoretical basis of social network analysis, and discusses the key techniques required to conduct this type of analysis. Specifically, it discusses issues of study design, data collection, and measures of social network structure.

Carrington PJ, Scott J, Wasserman S. Models and methods in social network analysisCambridge: Cambridge University Press, 2005. This book provides a more detailed methodological approach to social network analysis. Chapter 2 provides a brief discussion about study designs, while chapter 3 focus on methods of data collection and model fitting.

Wasserman S, Faust K. Social network Analysis: methods and applications. Cambridge: Cambridge University Press, 1994.

M.E.J Newman. Networks. An Introduction. 1st edition Oxford University Press, 2010 This book is an introductory text that discusses social networks and social network analysis.

Methodological Articles

Social Network Analysis: A Methodological Introduction Author(s): CT Butts Journal: Asian Journal of Social Psychology Year published: 2008

Survey Methods for Network Data

Author(s): PV Marsden Journal: The Sage Handbook of Social Network Analysis Year published: 2011

The Art and Science of Dynamic Network Visualization

Author(s): S Bender-deMoll, DA McFarland Journal: Journal of Social Structure Year published: 2006

Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms

Author(s): MT Rivera, SB Soderstrom, B Uzzi Journal: Annual Review of Sociology Year published: 2010

A glossary of terms for navigating the field of social network analysis

Author(s): P Hawe, C Webster, A Shiell Journal: J Epidemiol Community Health Year published: 2004

Network analysis in public health: history, methods, and applications

Author(s): DA Luke, JK Harris Journal: Annual Review of Public Health Year published: 2007

Application Articles

A (very) Short Introduction to R

Author(s): P Torfs, C Brauer Year published: 2012

A comparative study of social network analysis tools

Author(s): Combe et al Journal: France: Web Intelligence & Virtual Enterprises, Saint-Etienne Year published: 2010

Software for social network analysis

Author(s): M Huisman, MAJ van Duijn Journal: Models and methods in social network analysis Year published: 2005

The spread of obesity in a large social network over 32 years

Author(s): NA Christakis, JH Fowler Journal: New England journal of medicine Year published: 2007

Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic

Author(s): E Cohen-Cole, JM Fletcher Journal: Journal of health economics Year published: 2008

Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis

Author(s): E Cohen-Cole, JM Fletcher Journal: Bmj Year published: 2008

Structural characteristics of social networks and their relationship with social support in the elderly: Who provides support?

Author(s): TE Seeman, LF Berkman Journal: Social Science & Medicine Year published: 1988

Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California

Author(s): BY Lee, SM McGlone, Y Song, TR Avery, S Eubank, CC Chang, RR Bailey, DK Wagener, DS Burke, R Platt, SS Huang Journal: American Journal of Public Health Year published: 2011

Transmission network analysis in tuberculosis contact investigations

Author(s): VJ Cook, SJ Sun, J Tapia, SQ Muth, DF Argüello, BL Lewis, RB Rothenberg, PD McElroy Journal: J Infect Dis Year published: 2007

Description: R contains several packages relevant for social network analysis: igraph is a generic network analysis package; sna performs sociometric analysis of networks; network manipulates and displays network objects; PAFit can analyse the evolution of complex networks by estimating preferential attachment and node fitness; tnet performs analysis of weighted networks, two-mode networks, and longitudinal networks; ergm is a set of tools to analyze and simulate networks based on exponential random graph models exponential random graph models; Bergm provides tools for Bayesian analysis for exponential random graph models, hergm implements hierarchical exponential random graph models; 'RSiena' allows the analyses of the evolution of social networks using dynamic actor-oriented models; latentnet has functions for network latent position and cluster models; degreenet provides tools for statistical modeling of network degree distributions; and networksis provides tools for simulating bipartite networks with fixed marginals. Price: Free

Description: statnet is a suite of software packages that implement a range of network modeling tools. Price: Free

https://www.insna.org/ International Network for Social Network Analysis (INSNA) is a professional association for researchers interested in network analysis. The website contains SNA software descriptions, news, scholarly articles, technical columns, abstracts and book reviews. The site features graduate programs, courses, discussion forums, I-Connect, bibliographies and publications related to SNA. INSNA also provides a Journal of Social Networks and holds an Annual International Social Networks Conference and other SNA events.

Combe et al. (2010). A comparative study of social network analysis tools. France: Web Intelligence & Virtual Enterprises, Saint-Etienne

This article aims to describe the functionalities of social network analysis. In addition, the article explains and compares several of the widely used software tools that are dedicated to social network analysis. The software packages discussed in detail include Pajek, Gephi, NetworkX and igraph.

International Network for Social Network Analysis (INSNA) Website overview: International Network for Social Network Analysis (INSNA) is a professional association for researchers interested in network analysis. The website contains SNA software descriptions, news, scholarly articles, technical columns, abstracts and book reviews. The site features graduate programs, courses, discussion forums, I-Connect, bibliographies and publications related to SNA. INSNA also provides a Journal of Social Networks and holds an Annual International Social Networks Conference and other SNA events.

Website overview: Statnet is a suite of software packages for network analysis that implement recent advances in the statistical modeling of networks. The analytic framework is based on Exponential family Random Graph Models (ergm). statnet provides a comprehensive framework for ergm-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. statnet has a different purpose than the excellent packages UCINET or Pajek; the focus is on statistical modeling of network data. The statistical modeling capabilities of statnet include ERGMs, latent space and latent cluster models. The packages are written in a combination of (the open-source statistical language) R and (ANSI standard) C, and are called from the R command line. And because it runs in the R package ( www.r-project.org ), you also have access to the full functionality of R, including the packages "network" and "sna" written by Carter Butts. statnet has a command line interface, not a GUI, with a syntax that resembles R.

Host/program: University of Michigan/Coursera Course format: Online Software used: Gephia, Netlogo, R

Join the Conversation

Have a question about methods? Join us on Facebook

• Privacy Policy

Home » Social Network Analysis – Types, Tools and Examples

Social Network Analysis – Types, Tools and Examples

Table of Contents

Social Network Analysis

Social Network Analysis (SNA) is an analytical method used to study social structures through the use of networks and graph theory. It identifies the relationships between individuals, organizations, or other entities and examines the patterns and implications of these relationships.

The nodes in the network represent the actors within the networks and the ties or edges represent relationships between the actors. These might be, for example, friendship ties between people, business relationships between companies, or communication patterns between individuals.

By analyzing the network structure and the characteristics of the actors within the network, SNA can reveal properties such as the distribution of resources, the flow of information, or the overall connectivity of the network.

Here are a few key concepts in SNA:

• Centrality : This measures the importance of a node in the network. Various centrality measures exist, each emphasizing a different aspect of a node’s position within the network, such as degree centrality (the number of direct connections a node has), betweenness centrality (the number of times a node acts as a bridge along the shortest path between two other nodes), and eigenvector centrality (the sum of the centrality scores of all nodes that one node is connected to).
• Density : This is a measure of the proportion of possible connections in a network that are actual connections. A high density suggests that the network participants are highly interconnected.
• Clusters or Communities : These are groups of nodes that are more densely connected with each other than with the rest of the network.
• Structural Holes : These are gaps in the network where a node could potentially act as a bridge between two unconnected parts of the network.

Types of Social Network Analysis

Social Network Analysis can be broadly categorized based on the type of networks being analyzed, the level of analysis, and the methodologies employed. Here are a few ways to categorize SNA:

Whole Network Analysis

This type of analysis focuses on the structure and properties of the network as a whole. This might include measures of network cohesion, centralization, and density. It also looks at the overall distribution of relationships and identifies key groups or clusters within the network.

Ego Network Analysis

In this type of analysis, the focus is on a single actor (the ‘ego’) and their immediate network (the ‘alters’). It’s often used when interest is in the personal networks of individuals. Measures can include the size of the network, the composition of the network in terms of the types of ties and nodes, and measures of network density or diversity.

Two-mode (or Bipartite) Network Analysis

This type of SNA is used when there are two different types of nodes, and connections are only possible between nodes of different types (not within types). For example, authors and the books they write, or actors and the movies they appear in. In such a network, you can study the connections between nodes of one type, mediated by nodes of the other type.

Dynamic Network Analysis (DNA)

This is used to study how social networks evolve over time. This could involve studying how ties between actors develop or disappear, or how actors move around within the network. In addition to traditional network measures, DNA also considers measures that are dynamic in nature, such as change in centrality over time.

Semantic Network Analysis

This type of SNA focuses on the relationships between concepts or ideas, rather than individuals or organizations. For instance, semantic network analysis could map out how different scientific concepts are related to each other in the literature.

Social Media Network Analysis

A specialized form of SNA, this deals with the study of social relationships as expressed through social media platforms. It allows for the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities.

Social Network Analysis Techniques

Social Network Analysis involves various techniques to understand the structure and patterns of relationships among actors (people, organizations, etc.) in a network. These techniques may be mathematical, visual, or computational, and often involve the use of specialized software. Here are several common SNA techniques:

Network Visualization

One of the most basic SNA techniques involves creating a visual representation of the network. This can help to reveal patterns and structures within the network that may not be immediately obvious from the raw data. There are various ways to create such visualizations, depending on the specifics of the network and the goals of the analysis. Software such as Gephi or Cytoscape can be used for network visualization.

Centrality Measures

These are techniques used to identify the most important nodes within a network. Various measures of centrality exist, each highlighting different aspects of a node’s position in the network. These include degree centrality (the number of connections a node has), betweenness centrality (how often a node appears on the shortest path between other nodes), closeness centrality (how quickly a node can reach all other nodes in the network), and eigenvector centrality (a measure of the influence of a node in a network).

Community Detection

Also known as clustering, this technique aims to identify groups of nodes that are more closely connected with each other than with the rest of the network. This can help to reveal sub-groups or communities within the network.

Structural Equivalence and Blockmodeling

Structural equivalence is a measure of how similarly two nodes are connected to the rest of the network. Nodes that are structurally equivalent often play similar roles in the network. Blockmodeling is a technique used to simplify a network by grouping together structurally equivalent nodes.

Dynamic Network Analysis

This involves studying how a network changes over time. This can help to reveal patterns of network evolution, including how relationships form and dissolve, how centrality measures change over time, and how communities evolve.

Network Correlation and Regression

These are statistical techniques used to identify and test for patterns within the network. For example, one might use these techniques to test whether nodes with certain characteristics are more likely to form connections with each other.

Social Network Analysis Tools

There are several tools available that can be used to conduct Social Network Analysis (SNA). These range from open-source software to commercial offerings, each with their own strengths and weaknesses. Here are a few examples:

• Gephi : Gephi is an open-source, interactive visualization and exploration platform for all kinds of networks and complex systems. It’s user-friendly and allows users to interactively manipulate the network visualization, perform network analysis, and export results in various formats.
• UCINet : UCINet is a comprehensive package for the analysis of social network data as well as other 1-mode and 2-mode data. It’s widely used in social science research.
• NetDraw : Often used in conjunction with UCINet, NetDraw is a free tool for visualizing networks. It supports the visualization of large networks and allows for various customization options.
• Pajek : Pajek is a program for the analysis and visualization of large networks. It’s an extensive tool, offering a range of complex network metrics, and is free for non-commercial use.
• NodeXL : NodeXL is a free, open-source template for Microsoft Excel that allows users to display and analyze network graphs. Its integration with Excel makes it user-friendly, particularly for those already familiar with Excel.
• Cytoscape : Originally designed for biological research, Cytoscape is now a popular open-source software platform for visualizing complex networks and integrating these with any type of attribute data.
• SocioViz : SocioViz is a social media analytics platform for Twitter data, focused on network analysis and visualization. It’s a powerful tool for researchers interested in online social networks.
• NetworkX : NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It integrates well with other scientific Python tools like SciPy and Matplotlib.
• igraph : igraph is a library available in R, Python, and C for creating, manipulating, and analyzing networks. It’s highly efficient and can handle large networks.
• RSiena : RSiena is an R package dedicated to the statistical analysis of network data, with a particular focus on longitudinal social networks.

Social Network Analysis Examples

Social Network Analysis Examples are as follows:

• Public Health – COVID-19 Pandemic : During the COVID-19 pandemic, SNA was used to model the spread of the virus. The interactions between individuals were mapped as a network, helping identify super-spreader events and informing public health interventions.
• Business – Google’s “PageRank” Algorithm : Google’s PageRank algorithm, which determines the order of search engine results, is a type of SNA. It considers web pages as nodes and hyperlinks as connections, determining a page’s importance by looking at the number and quality of links to it.
• Sociology – Stanley Milgram’s “Small World” Experiment : This is one of the most famous social network experiments, where Milgram demonstrated that any two people in the United States are separated on average by only six acquaintances, leading to the phrase “six degrees of separation.”
• Online Social Networks – Facebook’s “People You May Know” Feature : Facebook uses SNA to suggest new friends. The platform analyzes your current network and suggests people you’re likely to know, typically friends of friends or people who share common networks.
• Criminal Network Analysis – Capture of Osama bin Laden : SNA was reportedly used in the operation to capture Osama bin Laden. By mapping the social connections of known associates, intelligence agencies were able to locate the Al-Qaeda leader.
• Academic Research – Collaboration Networks : SNA is used in scientometrics to analyze collaboration networks among researchers . For example, a study on co-authorship networks in scientific articles can reveal patterns of collaboration and the flow of information in different disciplines.

When to use Social Network Analysis

Social Network Analysis is a powerful tool for studying the relationships between entities (like people, organizations, or even concepts) and the overall structure of these relationships. Here are several situations when SNA might be particularly useful:

• Understanding Complex Systems : SNA is well-suited to studying complex, interconnected systems. If you’re interested in not just individual entities but also the relationships between them, SNA can provide valuable insights.
• Identifying Key Actors : SNA can help identify the most important entities in a network based on their position and connections. These might be influential people within a social network, critical servers in a computer network, or key scholars in a field of study.
• Studying Diffusion Processes : If you’re interested in how something (like information, behaviors, diseases) spreads through a network, SNA can be a valuable tool. It allows for the examination of diffusion pathways and identification of nodes that speed up or hinder diffusion.
• Detecting Communities : SNA can be used to identify clusters or communities within a network. These might be groups of friends within a social network, clusters of companies in a business network, or research clusters in scientific collaboration networks.
• Mapping Out Large Systems : In cases where you have a large system of many interconnected entities, SNA can provide a visual representation of the system, making it easier to understand and analyze.
• Investigating Structural Roles : If you’re interested in the roles individuals or entities play within their network, SNA offers methods to classify these roles based on the pattern of their relationships.

Purpose of Social Network Analysis

Social Network Analysis serves a wide range of purposes across different fields, given its versatile nature. Here are several key purposes:

• Understanding Network Structure : One of the key purposes of SNA is to understand the structure of relationships between actors within a network. This includes understanding how the network is organized, the distribution of connections, and the patterns of interaction.
• Identifying Key Actors or Nodes : SNA can identify crucial nodes within a network. These could be individuals with many connections, or nodes that serve as critical links between different parts of the network. In a business, for instance, such nodes might be key influencers or innovators.
• Detecting Subgroups or Communities : SNA can identify clusters or communities within a network, i.e., groups of nodes that are more connected to each other than to the rest of the network. This can be valuable in numerous contexts, from identifying communities in social media networks to detecting collaboration clusters in scientific networks.
• Analyzing Information or Disease Spread : In public health and communication studies, SNA is used to study the patterns and pathways of information or disease spread. Understanding these patterns can be critical for designing effective interventions or campaigns.
• Analyzing Social Capital : SNA can help understand an individual or group’s social capital – the resources they can access through their network relationships. This analysis can offer insights into power dynamics, access to resources, and inequality within a network.
• Studying Network Dynamics : SNA can examine how networks change over time. This could involve studying how relationships form or dissolve, how centrality measures change over time, or how communities evolve.
• Predicting Future Interactions : SNA can be used to predict future interactions or relationships within a network, which can be useful in a variety of settings such as recommender systems, predicting disease spread, or forecasting emerging trends in social media.

Applications of Social Network Analysis

Social Network Analysis has a wide range of applications across different disciplines due to its capacity to analyze relationships and interactions. Here are some common areas where it is applied:

• Public Health : SNA can be used to understand the spread of infectious diseases within a community or globally. It helps identify “super spreaders” and optimizes strategies for vaccination or containment.
• Business and Organizations : Companies use SNA to analyze communication and workflow patterns, enhance collaboration, boost efficiency, and detect key influencers within their organization. It can also be applied in understanding and leveraging informal networks within a business.
• Social Media Analysis : On platforms like Facebook, Twitter, or Instagram, SNA helps analyze user behavior, track information dissemination, identify influencers, detect communities, and develop recommendation systems.
• Criminal Justice : Law enforcement and intelligence agencies use SNA to understand the structure of criminal or terrorist networks, identify key figures, and predict future activities.
• Internet Infrastructure : SNA helps in mapping the internet, identifying critical nodes, and developing strategies for robustness against cyberattacks or outages.
• Marketing : In marketing, SNA can track the diffusion of advertising messages, identify influential consumers for targeted marketing, and understand consumer behavior and brand communities.
• Scientometrics : SNA is used in academic research to map co-authorship networks or citation networks. It can uncover patterns of collaboration and the flow of knowledge in scientific fields.
• Politics and Policy Making : SNA can help understand political alliances, lobby networks, or policy networks, which can be critical for strategic decision-making in politics.
• Ecology : In ecological studies, SNA can help understand the relationships between different species in an ecosystem, providing valuable insights into ecological dynamics.

Advantages of Social Network Analysis

Social Network Analysis offers several advantages when studying complex systems and relationships. Here are a few key advantages:

• Reveals Complex Relationships : SNA allows for the study of relationships between entities (be they people, organizations, computers, etc.) in a way that many other methodologies do not. It emphasizes the importance of these relationships and helps reveal complex interaction patterns.
• Identifies Key Players : SNA can identify the most influential or important nodes in a network, whether they are individuals within a social network, key servers in an internet network, or central scholars in an academic field.
• Unveils Network Structure and Communities : SNA can help visualize the overall structure of a network and can reveal communities or clusters of nodes within a network. This can provide valuable insights into the organization and division of a network.
• Tracks Changes Over Time : Dynamic SNA allows the study of networks over time. This can help to track changes in the network structure, the role of specific nodes, or the flow of information or resources through the network.
• Helps Predict Future Interactions : Based on the analysis of current and past relationships, SNA can be used to predict future interactions, which can be useful in many fields including public health, marketing, and national security.
• Aids in Designing Effective Strategies : The insights gained from SNA can be used to design targeted strategies, whether that’s intervening in the spread of misinformation online, designing a targeted marketing campaign, disrupting a criminal network, or managing collaboration in an organization.
• Versatility : SNA can be applied to a vast array of fields, from sociology to computer science, biology to business, making it a versatile tool.

Disadvantages of Social Network Analysis

While Social Network Analysis is a powerful tool with wide-ranging applications, it also has certain limitations and disadvantages that are important to consider:

• Data Collection Challenges : Collecting complete and accurate network data can be a major challenge. For larger networks, it may be nearly impossible to collect data on all relevant relationships. There’s also a risk of response bias, as people may forget, overlook, or misinterpret their relationships when providing data.
• Time and Resource Intensive : Collecting network data, especially from primary sources, can be extremely time-consuming and expensive. Additionally, analyzing network data can also require significant computational resources for larger networks.
• Complexity : SNA involves complex concepts and measures, which can be difficult to understand without specialized knowledge. This complexity can make it difficult to communicate findings to a non-technical audience.
• Privacy and Ethical Concerns : SNA often involves sensitive data about individuals’ relationships and interactions, raising important privacy and ethical concerns. It’s important to handle this data carefully to respect individuals’ privacy.
• Static Snapshots : Traditional SNA often provides a static snapshot of a network at a particular point in time, which may not capture the dynamic nature of social relationships. While dynamic SNA does exist, it adds additional complexity and data demands.
• Dependence on Quality of Data : The insights and conclusions drawn from SNA are only as good as the data used. Incomplete, inaccurate, or biased data can lead to misleading results.
• Difficulties in Establishing Causality : While SNA can reveal patterns and associations in network data, it can be difficult to establish causal relationships. For instance, do strong connections between two individuals lead to similar behavior, or does similar behavior lead to strong connections?
• Assumptions about Relationships : SNA often assumes that relationships are equally important, which might not always be the case. Different relationships might have different strengths or meanings, which can be challenging to represent in a network.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Digital Ethnography – Types, Methods and Examples

What is Data Science? Components, Process and Tools

Predictive Analytics – Techniques, Tools and...

Blockchain Research – Methods, Types and Examples

Prescriptive Analytics – Techniques, Tools and...

Diagnostic Analytics – Methods, Tools and...

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.8(4); 2022 Apr

Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020

Adhe rizky anugerah.

a Bioresource Management Lab, Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Prafajar Suksessanno Muttaqin

b Department of Logistics Engineering, School of Industrial and System Engineering, Telkom University, 40257 Bandung, Indonesia

Wahyu Trinarningsih

c Faculty of Economics and Business, Universitas Sebelas Maret, 57126 Surakarta, Indonesia

Associated Data

Data included in article/supplementary material/referenced in article.

In the past years, research in Social Network Analysis (SNA) has increased. Initially, the research area was limited to sociology and anthropology but has now been used in numerous disciplines. The business and management discipline has many potentials in employing the SNA approach due to enormous relational data, ranging from employees, stakeholders to organisations. The study aims to analyse the research trend, performance, and the utilisation of the SNA approach in business and management research. Bibliometric analysis was conducted by employing 2,158 research data from the Scopus database published from 2001 to 2020. Next, the research quantity and quality were calculated using Harzing's Publish or Perish while VOSviewer visualised research topics and cluster analysis. The study found an upward trend pattern in SNA research since 2005 and reached the peak in 2020. Generally, six subjects under the business and management discipline have used SNA as a methodology tool, including risk management, project management, supply chain management (SCM), tourism, technology and innovation management, and knowledge management. To the best of the authors' knowledge, the study is the first to examine the performance and analysis of SNA in the overall business and management disciplines. The findings provide insight to researchers, academicians, consultants, and other stakeholders on the practical use of SNA in business and management research.

Social network analysis; Bibliometrics; Clustering analysis; Business and management; Literature.

1. Introduction

The SNA is a theory investigating the relations and interactions based on anthropology, sociology, and social psychology to assess social structures ( Erçetin and Neyişci, 2014 ). The social structure in a network theory comprises individuals or organisations named nodes linked through one or more types of interdependencies, such as friendship, kinship, financial exchange, knowledge or prestige ( Parell, 2012 ). The actors range across different levels, from individuals, web pages, families, large organisations, and nations. Nowadays, SNA usage has grown, utilised in anthropology and sociology and several fields of science, including business and management disciplines.

However, studies on the SNA trends and applications in business and management are limited. Although published articles provide a catalogue of SNA concepts, they lack explanatory mechanisms on its application ( Borgatti and Li, 2009 ). Thus, the study aims to assess publication performances and explore SNA usage in business and management studies using Bibliometric analysis. The bibliometric methodology has been widely used to provide quantitative analysis of written publications using statistical tools ( Ellegaard and Wallin, 2015 ). It can help detect established and emergent topical areas, research clusters and scholars, and others ( Fahimnia et al., 2015 ). This analysis reveals important publications and objectively depicts the linkages between and among articles about a specific research topic or field by examining how frequently they have been co-cited by other published articles ( Fetscherin and Usunier, 2012 ).

Bibliometric analysis has at least two primary objectives: 1) to quantitatively measure the quality of journals or authors using statistical indicators such as citations rates ( Vieira et al., 2021 ), and 2) to analyse the knowledge structure and development of specific research fields ( Jing et al., 2015 ). Hence, the study addresses the following research questions: RQ1: What is the current research trend of SNA in business and management research ? RQ2: What is the most productive year of SNA in the business and management discipline ? RQ3: What are the most influential and productive institutions, authors, journals, and countries ? RQ4: What is the use of SNA in the business and management discipline and their cluster topics in the past 20 years ?

Several literature reviews and bibliometric papers on the use of SNA in general business and management areas have been published, but the number is limited. Monaghan, Lavelle, & Gunnigle (2017) analysed SNA usage in management research and practice to discuss the critical dimensions for handling and analysing network data for business research. The authors discovered four dimensions in initial engagement with SNA in business and management research: structure of research design, data collection, handling of data and data interpretation. Nonetheless, studies did not explain the distribution of research clusters and how SNA can be used in practical business and management research. Specifically, Su et al. (2019) conducted a Bibliometric analysis on SNA literature with no limitation of subject discipline and collected the data from Web of Science (WoS), covering 20 publication years from 1999 to 2018. Nevertheless, Su et al. (2019) mainly discussed the SNA publication performance but not how the approach was used previously.

In the more specific subject area of business and management, SNA has been explored to unveil the relationship between organisations, as conducted by Sozen et al. (2009) . The SNA has been used to measure the organisations' social capital, map resource dependency relations, and discover coalitions and cliques between organisations. Kurt and Kurt (2020) have explored the potential of SNA in international business (IB) research, because of two fundamental phemomena: firm internationalisation and multinational enterprises (MNEs). From the marketing perspective, SNA could detect the most influential actors to efficiently spread a message in online communities for marketing purposes ( Litterio et al., 2017 ).

The current study conducted a clustering analysis to identify and analyse SNA performance and its application in general business and management discipline using bibliometrics information. Thus, academicians, managers, consultants, and other stakeholders could understand when and how to apply the SNA approach. For instance, SNA can identify potential risks contributing to schedule delays in project risk management ( Li et al., 2016 ). The discussion section explores how SNA has been previously used in business and management research. Besides, the study addresses the problem in Borgatti and Li (2009) , exploring the actual application of SNA in management and business research.

2.1. Data sources and search strategy

The primary study objective is to analyse the research trend and explore the SNA approach in business and management research. A Bibliometric analysis was employed due to its accuracy in quantifying and evaluating scientific publications ( Carmona-Serrano et al., 2020 ). Additionally, the data were collected through the Scopus database. Although Scopus and WoS are the main and most comprehensive sources for Bibliometric analysis, Scopus has more advantages: more inclusive content coverage, more openness to society, and available individual profiles for all authors, institutions, and serial sources. Additionally, many papers have confirmed that Scopus provides wider overall coverage and Scopus indexing a greater amount of unique sources not covered by WoS ( Pranckutė, 2021 ). In the business, economics, and management area, 89% of articles listed in WoS are listed in Scopus. Hence, the study area (business and management) chose the Scopus database for further analysis.

The next step involved determining the search string, including all documents with the title, abstract, and keywords containing "network analysis" or "Social Network Analysis". These two main keywords are representative enough to reach the objective; they are not too wide and specific. The main goal is the utilisation of SNA as a concept and as a methodology can be widely captured. These two versions of keywords “Social Network Analysis” and “Network Analysis” without the “social” has a significant impact. Some articles did not put the complete sentence of SNA, although the articles mainly discussed the concept of network analysis. One of the examples is the ownership structure related research developed by Vitali et al. (2011) which changed the word "social network analysis" to "corporate network analysis". The term “social” in SNA refers to people interaction, while in the operation research, the relationship could be between airport, stakeholders, corporation, etc.

In the first run, 113,945 research related to SNA was found in the Scopus database, mainly Engineering and Computer Science fields. Besides, the search results were limited to the subject area in business, management, and accounting and covered publication from 2001 to 2020 (20 years). The study also excluded non-journal articles, such as conference proceedings, trade reports, book chapters, and others.

The search limitations have resulted in 2,881 articles, but many were still not related to network analysis or business and management. Further, 723 articles were excluded, covering articles in neuroscience, bibliometric, circuit network (engineering), earth and planetary science, chemistry, etc., although the articles employed network analysis as a methodology. The exclusion was also applied to articles that use SNA in multi-subject journals with little or no explanation in business, management, and accounting perspectives. One example is the Journal of Cleaner Production listed in four subject areas: business, management and accounting; energy; industrial and manufacturing engineering; and environmental science. In this journal, SNA theory is used to identify the relationship between ecosystems by measuring the flow of energy or material between organisms, which has little or no explanation from business and management perspectives. At the end of the search, 2,158 articles were extracted for further analysis. The flow chart on the data collection strategy is presented in Figure 1 .

The search strategy flow diagram (adopted from ( Zakaria et al., 2021 )).

2.2. Data analysis

The first stage involved analysing the data descriptively to identify the quality and quantity using standard Bibliometric measures ( Hirsch, 2005 ). The total number of publications (TP) assessed the quantity dimension, whereas other metrics assessed the quality dimension, such as total citation (TC), number of cited publications (NCP), average citation per publication (C/P), average citations per cited publication (C/CP) ( Hirsch, 2007 ). Additionally, the g-index ( g ) and h-index ( h ) are usually included in the Bibliometric measures to predict future achievement rather than standard measures. The indicators are applied to various levels: country-level, organisation-level, journal-level, and author-level. The information is processed and analysed using Harzing publish or perish (PoP) software by extracting Research Information System (RIS) data from the Scopus database.

The second stage visualised the research network to understand the relationship between nodes, including authors, affiliations (organisations), countries, citations, and keywords. Nonetheless, only keywords co-occurrence was carried out to examine the past, current, and future potential of SNA in business and management research. The study analysed the keywords based on the frequency, edges, and clusters. The combination between nodes (keywords) and edges (the relationship between keywords) form clusters with numerous research themes ( Dhamija and Bag, 2020 ). The bigger nodes show a higher occurrence in the keyword visualisations, and the thicker edges show the higher link strength. Meanwhile, cluster analysis in the study represents a set of similar keywords in one group, different in other groups to identify the research interest and keywords combination within the group. The cluster mapping was performed by VOSviewer, an open-access programme to construct and view Bibliometric maps ( van Eck and Waltman, 2010 ). Besides, the study developed an overlay visualisation to explore research evolution in SNA over time.

3.1. Description of retrieved literature

The study is limited to SNA research in business and management research published between 2001 to 2020. The study also excluded review papers, conference papers, editorials, and other documents besides journal articles for further analysis. Ultimately, the study retrieved a total of 2,158 articles. Although the search was limited to only English articles, the study identified seven bilingual articles in Spanish (3 articles), Chinese (2 articles), Lithuanian (1 article), and Portuguese (1 article). The articles had 58,522 citations, an average of 2,926 citations per year, and 27 citations per paper. The complete citation metrics for the articles are shown in Table 1 .

Table 1

Citations metrics.

Besides SNA as a primary keyword, the top keywords were "innovation" (6.16%), "project management" (3.48%), "knowledge management" (3.29%), "decision making" (2.97%), "complex networks" (2.69%), and others. The top keywords are listed in Table 2 . High-frequency keywords show the popularity of a specific topic ( Pesta et al., 2018 ). The listed keywords in the study usually appear together in SNA and are used to explore the potential use of SNA in business and management research.

Table 2

Top keywords.

3.2. Research Growth

Although SNA research productivity presented the ups and downs throughout the year, a consistent upward trend was found in the pattern. Research in the first five years (2001–2005) was limited and never reached 30 publications per year. In 2002, only 10 articles were published, increasing almost five times in 2007 (n = 49 documents). The number increased until 2020, slightly decreasing in 2011, 2015, and 2019. Meanwhile, the most productive year was in 2020, with 334 published articles.

The articles published in 2001 had the highest average citation per publication (c/p = 186.69). However, the highest h-index were in 2010 ( h = 43) and 2014 ( h = 36) which indicate high cumulative impact of the articles measured by its quantity with quality. Low citations per publication in recent years were expected due to increasing citation counts over time. The publication trend and average citations per publication are presented in Figure 2 .

Total publications and citations by year.

3.3. Top countries, institutions, and authors in SNA business and management research

The United States (US), the United Kingdom (UK), and China were the most prolific countries with 605, 230 and 215 articles, respectively. The study discovered no dominating continent that produced SNA business and management research, and all were equally distributed except for Africa. The top ten countries list (see Table 3 ) showed one North American, four Asian and Oceanian, and five European countries. As the top most productive countries, the United States and the United Kingdom published quality articles with highest average citation per publication of 39.34 and 32.80, respectively. Meanwhile, Asian countries had small citations (based on the top ten most productive countries): South Korea (c/p = 21.84) and China (c/p = 27.08) were at the bottom of the ranking according to the c/p calculation.

Table 3

The top ten countries contributed to the publications.

Notes: TP = total number of publications; NCP = number of cited publications; TC = total citations; C/P = average citations per publication; C/CP = average citations per cited publication; h = h-index; and g = g-index.

Hong Kong Polytechnic University was the most productive institution with 45 published articles and had the highest h- and g- index (see Table 4 ). The publication number was higher than the second most productive, Università Bocconi (n = 25). However, the top ten list showed that the University of Arizona had the highest c/p with 102.47, followed by the University of Kentucky (c/p = 58.40) and the Università Bocconi (c/p = 58.36). As the most productive country, there were four United States universities listed as the top most productive institutions but it only covered 10.9% from the total US articles. This indicates that the publications were distributed to other US institutions. Nevertheless, articles from two Hong Kong insitutions, Hong Kong Polytechic University and City University Hongkong accumulated 57 articles (82.61% of the country's total articles).

Table 4

Top 10 most influential institutions in SNA (business and management) research.

The two most productive institutions conducted different research themes. Figure 3 shows that Hong Kong Polytechnic University use SNA in numerous areas, such as "supply chain management", "transportation", "big data", "knowledge management", “stakeholder analysis in construction project” and others. Nonetheless, Università Bocconi research mostly involved tourism; the keywords were "tourist destination", "tourism management", "stakeholder", and “hospitality”. Besides, 17 of the 25 articles (68.0%) from Università Bocconi were written by Rodolfo Baggio, the author with the most article in SNA business and management. Table 5 presents the most productive authors with at least six published articles in SNA.

Research topic comparison between A) Hong Kong Polytechnic University and B) Università Bocconi.

Table 5

Most productive authors with a minimum of six articles.

Rodolfo Baggio was the most productive author with the most publications, followed by Noel Scott (n = 8) from Australia and Andrea Fronzetti Colladon (n = 7) from Italy. Based on the average citations per document, Carlos Casanueva from the Universidad de Sevilla, Spain, had the highest score with an average of 95.33 citations per document. Baggio, R. and Scott, N, as the most productive authors have similar research interest, which is in tourism management. Based on the study database, they had written four articles together using a network analysis approach in tourism management. Fronzetti Colladon, A. is an expert in big data, creativity and innovation management while Hossain L., utilising SNA in organisational communication network during crisis or emergency events.

3.4. Most active journals

The majority of the articles were mostly published in Elsevier's journals. Specifically, seven out of 12 source titles were Elsevier's journals; two journals were published by the American Society of Civil Engineers (ASCE), two journals by Taylor's and Francis, and one by Emerald. The Journal of Technological Forecasting and Social Change was the most active source with 84 articles, followed by Transportation Research Part E: Logistics and Transportation Review and Knowledge based Systems with 45 and 44 articles, respectively. Based on the average citations per publication, Construction Management and Economics had the highest score (c/p = 78.50), followed by Decision Support Systems (c/p = 55.33) and Transportation Research Part E (c/p = 50.51). Table 6 demonstrates a list of the most active sources in publishing research in SNA (business and management) and its impact score (cite score and SCImago Journal Rank (SJR) 2019).

Table 6

Most active source title.

Notes: TP = total number of publications; C/P = average citations per publication.

The SNA usage in business and management varies and depends on the journal scope. Particularly, SNA is used to study the interaction between people in the social environment and in various other subjects. The use of SNA in specific journals was explored by visualising the network of keywords relationship, as presented in Figure 4 . The selected three journals publishing research in SNA (business and management) had a different perspective in employing SNA as a tool to analyse the relationship between nodes.

Most frequent keywords in a) Technological Forecasting and Social Change; b) Transportation Research Part E: Logistics and Transportation Review; and c) Journal of Business Research.

Journal of Technological Forecasting and Social Change primarily utilised "innovation", "technological development", "patents and inventions", "technology adoption", "emerging technology", and others. SNA can also be used for transportation research as published in Transportation Research Part E: Logistics and Transportation Review with keywords "numerical model", "transportation planning", "air transportation", "optimisation", "freight transport", and others. Meanwhile, research published in Journal of Business Research published articles with keywords “business networks”, “social closure”, “collaboration”, “diffusion”, and others. The SNA can also be used in construction and project management, tourism management, urban planning and development, and organisational study (coordination and competition).

3.5. Highly-cited articles

Tsai (2002) published the top-cited article in SNA business and management research titled " Social structure of "coopetition" within a multiunit organisation: Coordination, Competition, and Intra organisational Knowledge Sharing" . The publication had 1,124 or 56.2 citations per year. Besides, the article explored another use of SNA, explained in the "most active journals" section in the organisational study. The study revealed that most of the top 15 articles related to inter- or intra- organisational networks, and some papers explored the use of SNA in social communication, supply chain, tourism marketing, and others. Table 7 shows the top 15 highly-cited articles.

Table 7

Top 15 Highly-cited articles.

3.6. The use of SNA in business and management research

The study conducted a keywords cluster analysis to highlight SNA usage in business and management research and identify how keywords are linked. The keywords cluster analysis was presented in two ways; the first is based on the occurrence level (see Figure 5 ), and the second is based on the year of publications (see Figure 6 ). Based on the level of keywords occurrence, SNA research was classified into six clusters: cluster 1 (red nodes) covered research in construction, project management, and information management; cluster 2 (green nodes) covered research in transportation and tourism management; cluster 3 (dark blue nodes) covered research in semantic, big data, and decision support system; cluster 4 (yellow nodes) included research in innovation, international trade, and globalisation; cluster 5 (purple node) explored research in knowledge management and knowledge sharing; cluster 6 (light blue nodes) included research in social capital, and financial performance and management.

Keywords analysis of SNA in business and management publications.

Keywords evolution of SNA in business and management research.

According to publication years, the study discovered that from 2012 to 2014, the most frequent keywords were "project management", "optimisation", "technology transfer", and "construction industry". From 2015 to 2016, the keywords shifted to "data mining", "information management", "decision-making", "tourist destination", "air transportation", "airline industry", and "innovation". Recently, "sentiment analysis", "text mining", and "big data" became popular in SNA research.

4. Discussion

The study was conducted to analyse SNA research in business and management subjects. Generally, an upward trend was found in the number of publications, significantly increasing since 2005. A significant increase was also discovered in 2020, a 40.3% increase from the previous year, the second-highest increase in the past 20 years. A similar Bibliometric analysis on SNA research without subject limitation had a similar pattern with the study, whereby SNA publications increased gradually since 2005 Based on quality metrics, articles published in 2001 and 2002 had the highest average citations per publication. Moreover, several articles published in those years also had the highest number of citations published in organisational science.

Tsai (2002) articles had the highest citation number (c = 1,124), followed by Reagans and Zuckerman (2001) , with 1009 citations. Tsai (2002) displayed intra organisational as a set of social networks and examined networks of collaborative and competitive ties within the organisation. Each unit collaborates for knowledge sharing and competes for resources and market share. Additionally, the centrality concept was used to measure the ability of intra organisational units in the knowledge sharing behaviour. In the same journal, Reagans and Zuckerman (2001) employed the SNA approach to examine the relationship between team density and heterogeneity to its performance using two network metrics: network density to assess communication frequency between team members, and network heterogeneity to explore time allocation of scientists to colleagues far removed in the team tenure distribution. According to above explanation, SNA can be used at different organisation levels, one in unit levels-organisation, and another in person-level interactions under one team.

The study revealed the US, the UK, and China as the most productive countries. Moreover, the study had similar findings as in Su et al. (2019) ; they found that the US had dominated the research in SNA, UK ranked second, and followed by China. Based on the average c/p, institutions from Asia such as South Korea, China, and Taiwan (average c/p = 23.77) received lower scores than European institutions (average c/p = 28.31). Better quality and impactful research are needed for authors from Asian institutions. The following section describes SNA usage in business and management themes.

4.1. Research themes

The SNA is a set of formal methods for studying social structures according to graph theory. Individuals and social actors, such as groups and organisations, are shown in points and their social relations in lines ( Korom, 2015 ). Meanwhile, the structure relations and the location of individual actors have substantial behavioural consequences for individuals and social structure as a whole ( Wellman, 1988 ). The SNA has become a multidisciplinary endeavour extending beyond sociology and social anthropology sciences and to many other disciplines, such as politics, epidemiology, communication science, and others. The next section explores the use of SNA in business and management discipline from publication records between 2001 to 2020 by analysing the keywords co-occurrences.

4.2. Project management

The first paper that employed SNA in project management was published in 1997 by Loosemore, believing that construction project participants are embedded in complex social networks that are constantly changing. Furthermore, Loosemore (1997) analysed communication efficiency in the engineering project organisation during crises. Besides, Pryke (2004) had the highest citations in the construction and project management area. Contrary to Loosemore, who applied network analysis at the individual level, Pryke examined the relationship between project actors at the firm level. Pryke also used network analysis in the comparative analysis of procurement and project management of construction projects. Subsequently, SNA publication in project management literature increased substantially.

Chinowsky, Diekmann, & O'Brien (2010) summarised the use of network analysis in project management research. After examining communication efficiency, network analysis analysed networks in relationship-based procurement, the effect of centrality on project coordination, the effect of cultural diversity on project performance, collaboration effectiveness to achieve high-performance teams, and others. The study discovered several popular keywords in project management research, such as "communication", "decision-making", "stakeholder", "accident prevention", "scheduling", "information exchange", "collaborative projects" and others, which explained the use of SNA in this subject. The top three journals publishing SNA usage in project and construction management were the Journal of Construction Engineering and Management (ASCE), International Journal of Project Management (Elsevier), and Construction Management and Economics (Taylor & Francis).

4.3. Risk management

The SNA publications in risk assessment and management usually relate to other subjects, such as project management, SCM, knowledge management, and others. The study found 42 related articles, mostly on construction and project management. Notably, SNA improved the effectiveness and accuracy of stakeholder and risk analysis in green building projects ( Yang et al., 2016 ). The model considered the risk associated with stakeholders and the interdependencies of risks for better decision-making. Additionally, Li et al. (2016) employed SNA for risk evaluation and risk response processes in construction projects.

The SNA effectively evaluates the potential risk contributing to schedule delays in project processes by removing key nodes and links, ultimately removing stakeholder risk that is highly interconnected to another risk. Similar to Li, Yu et al. (2017) specifically utilised SNA for social risk in urban redevelopment projects during the housing demolition stage with an identical process. The approach extends beyond construction and project management subject and any other subjects that employed risk assessment or evaluation in their risk management process.

4.4. tourism management

The SNA approach in tourism-related subjects was widespread, with "tourist destinations" and “tourism” among the most frequent and top 25 keywords (see Table 2 ). Besides, Baggio, R. was the most productive author who used SNA in tourism research. Three research streams are related to network analysis in the travel industry setting: the Bibliometric analysis on research collaboration and knowledge creation; network analysis on the travel industry supply, destination, and policy systems; and tourist movements and behavioural patterns ( Liu et al., 2017 ). Besides, the study discovered three significant journals with the most publications in tourism research: Annals of Tourism Research, Tourism Management, and Current Issues in Tourism, similar to Casanueva et al. (2016) ; whereby tourism management was the most productive journal. Recently, the Annals of Tourism Research surpassed Tourism Management.

Most tourism supply and destination research highlighted collaboration and partnership among tourism stakeholders. Baggio, Scott and Cooper (2010) applied network analysis to explain the topology of stakeholders in Elba, Italy's tourism organisations (hotels, travel agencies, associations, public bodies, and others). The tourism stakeholders were described in quantitative (network metrics) and qualitative (figure) ways. For instance, the percentage of non-connected networks described the sparseness of a network and showed a low degree of collaboration or cooperation between stakeholders. Nonetheless, Leung et al. (2012) and Asero et al. (2016) , and others studied tourist movement and behavioural patterns by analysing tourists' itineraries with traditional (interview or online travel diaries) or more-advanced technologies (geographic information system (GIS), global positioning system (GPS), timing systems, camera-based systems, and others). The primary objective is to analyse the main tourist attraction, main tourism movement patterns and change patterns in tourist attractions.

4.5. Supply chain management

The use of SNA in supply chain management research had developed in 2010, and numerous scholars were unaware of the possibilities of the SNA approach in the SCM field ( Wichmann and Kaufmann, 2016 ). The supply chain is a network of companies comprising interconnected actors, such as suppliers, manufacturers, logistic providers, and customers ( Bellamy et al., 2014 ). Three journals with the most SNA articles in SCM are the International Journal of Production Economics, International Journal of Production Research, and Journal of Operations Management.

Y. Kim et al. (2011) employed SNA to analyse the structural characteristics of supply networks in a buyers-suppliers network of automotive industries. The networks in the supply chain were classified into the material flow (supply load, demand load, and operational criticality) and the contractual relationship between actors (influential scope, informational independence, and relational mediation). The study discovered that network metrics could be used to analyse the characteristic of supply network structures.

The SNA can also measure and reduce supply chain complexity ( Allesina et al., 2010 ), a concept based on ecological theory. Additionally, eight entropic performance indexes were used: total system throughput, average mutual information, development capacity, overhead in input, export, and dissipation, etc. Contrarily, Ting & Tsang (2014) utilised SNA to identify the possibility of counterfeit products from infiltrating into the supply chain using the transaction records history to detect problematic parties and their suspicious trails. Three SNA measures were included in the study: degree centrality, betweenness centrality, and closeness centrality.

4.6. Knowledge management

The SNA usage in knowledge management varies; for instance, Parise (2007) used SNA for knowledge management of human resources, including knowledge creation and innovation, knowledge transfer and retention, and job succession planning. The SNA in knowledge creation and innovation is used to identify the flow of ideas and bottlenecks in the decision-making process. The SNA is also applied to analyse the structure of regional knowledge in the technology specialisation ( Cantner et al., 2010 ), knowledge transfer analysis on sustainable construction projects ( Schröpfer et al., 2017 ), predicting and evaluating future knowledge flows in insurance organisations ( Leon et al., 2017 ) and knowledge transfer from experts to newcomers ( Guechtouli et al., 2013 ), and others. The top productive journals are the Journal of Knowledge Management, Technological Forecasting and Social Change, and the Journal of Construction Engineering and Management.

4.7. Technology and innovation management

The SNA is used for innovation and technology transfer. Keywords under this subject include "citation "innovation", "patents and inventions", "technological development", and others. The SNA metrics utilised to assess the performance and centrality of individuals in virtual research and design (R&D) groups by analysing their e-mails ( Ahuja et al., 2003 ), identifying the position and relationships between innovators ( Cantner and Graf, 2006 ), research collaboration network between university-industry ( Balconi and Laboranti, 2006 ), and others.

The SNA in patents and inventions identify companies with a significant legal influence on the applied technologies by analysing intellectual property lawsuits between companies (H. Kim and Song, 2013 ). Patents data were also popular to study technological innovation of electronic companies; SNA was employed to cluster the patents and find vacant technology domains ( Jun and Sung Park, 2013 ). Furthermore, patent data in SNA enables exploring the technology evolution of certain products ( Lee et al., 2010 ). Specifically, the top three journals were: Technological Forecasting and Social Change, Technological Analysis and Strategic Management, and Industry and Innovation.

5. Conclusion

After SNA was introduced in 1969 by Mitchell, many researchers from various fields were interested in studying the relationship between nodes. The most frequent disciplines that used SNA are sociology, anthropology, social psychology, and communication. Nevertheless, SNA usage in business and management discipline was limited. Hence, the study analysed the trend and performance of SNA in the business and management discipline from 2001 to 2020. The study revealed a steady upward trend of publications in this field and increased significantly since 2005. The US, the UK, and China were the most productive countries. Although the study found three Asian institutions as the most productive countries, the average c/p was lower than the European and American countries. Besides, SNA as a research tool has been published in multidisciplinary journals, ranging from Journal of Management in Engineering to Journal of Knowledge Management, depending on the subject of investigation.

The study also performed a co-occurrence keywords analysis to examine the research cluster and emerging research topics in SNA, especially in business and management studies. The study revealed six clusters, each containing one to two research disciplines. The SNA has been employed in numerous topics, including project management, risk management, tourism management, supply chain, knowledge management, and technological management. Observably, big data, social media and sentiment analysis are the trending topic in SNA.

The research contributions include: first, the publication trend and research productivity show the current issue and development of SNA in the business and management discipline; second, the data on most productive authors and institutions academic communication and cooperation among scholars in related fields; and lastly, the visualisation of research topics mapping and the cluster analysis explored the current use of SNA in different discipline and formulated future research agenda.

The study limitations are: first, the study only considered the Scopus database and SNA literature could be more extensive. Other significant databases, such as WoS and CNKI (Chinese National Knowledge Infrastructure) should be considered for future research. Second, in the co-occurrence keywords analysis, a threshold was set to limit important keywords; thus, the study might not include several research topics using SNA. Third, although the study has cleaned the database, titles not purely from business and management disciplines might be included due to journal sources with multi subject classification. Lastly, we only employed standard bibliometric measures as a quantitative assessment in this research; the inclusion of SNA’ centrality index can be considered in future study to assess the power and importance of authors, institutions, countries, and journals.

Institutional review board statement

Not applicable.

Informed consent statement

Decalarations, author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

• Acedo F.J., Barroso C., Casanueva C., Galán J.L. Co-authorship in management and organizational studies: an empirical and network analysis. J. Manag. Stud. 2006; 43 (5):957–983. [ Google Scholar ]
• Ahuja M.K., Galletta D.F., Carley K.M. Individual centrality and performance in virtual R& D groups: an empirical study. Manag. Sci. 2003; 49 (1):21–38. [ Google Scholar ]
• Allesina S., Azzi A., Battini D., Regattieri A. Performance measurement in supply chains: new network analysis and entropic indexes. Int. J. Prod. Res. 2010; 48 (8):2297–2321. [ Google Scholar ]
• Asero V., Gozzo S., Tomaselli V. Building tourism networks through tourist mobility. J. Trav. Res. 2016; 55 (6):751–763. [ Google Scholar ]
• Baggio R., Scott N., Cooper C. Network science: a review focused on tourism. Ann. Tourism Res. 2010; 37 (3):802–827. [ Google Scholar ]
• Balconi M., Laboranti A. University–industry interactions in applied research: the case of microelectronics. Res. Pol. 2006; 35 (10):1616–1630. [ Google Scholar ]
• Bellamy M.A., Ghosh S., Hora M. The influence of supply network structure on firm innovation. J. Oper. Manag. 2014; 32 (6):357–373. [ Google Scholar ]
• Borgatti S.P., Li X. On social network analysis in a supply chain context. J. Supply Chain Manag. 2009; 45 (2):5–22. [ Google Scholar ]
• Boschma R.A., ter Wal A.L.J. Knowledge networks and innovative performance in an industrial district: the case of a footwear district in the South of Italy. Ind. Innovat. 2007; 14 (2):177–199. [ Google Scholar ]
• Brown J., Broderick A.J., Lee N. Word of mouth communication within online communities: conceptualizing the online social network. J. Interact. Market. 2007; 21 (3):2–20. [ Google Scholar ]
• Cantner U., Graf H. The network of innovators in Jena: an application of social network analysis. Res. Pol. 2006; 35 (4):463–480. [ Google Scholar ]
• Cantner U., Meder A., ter Wal A.L.J. Innovator networks and regional knowledge base. Technovation. 2010; 30 (9–10):496–507. [ Google Scholar ]
• Carmona-Serrano N., López-Belmonte J., Cuesta-Gómez J.-L., Moreno-Guerrero A.-J. Documentary analysis of the scientific literature on autism and technology in web of science. Brain Sci. 2020; 10 (12):985. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Casanueva C., Gallego Á., García-Sánchez M.-R. Social network analysis in tourism. Curr. Issues Tourism. 2016; 19 (12):1190–1209. [ Google Scholar ]
• Chinowsky P.S., Diekmann J., O’Brien J. Project organizations as social networks. J. Construct. Eng. Manag. 2010; 136 (4):452–458. [ Google Scholar ]
• Dhamija P., Bag S. Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal. 2020; 32 (4):869–896. [ Google Scholar ]
• Ellegaard O., Wallin J.A. The bibliometric analysis of scholarly production: how great is the impact? Scientometrics. 2015; 105 (3):1809–1831. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Erçetin Ş.Ş., Neyişci N.B. Chaos, Complexity and Leadership. 2014. Social network analysis : a brief introduction; pp. 167–171. [ Google Scholar ]
• Fahimnia B., Sarkis J., Davarzani H. Green supply chain management: a review and bibliometric analysis. Int. J. Prod. Econ. 2015; 162 :101–114. [ Google Scholar ]
• Fetscherin M., Usunier J.C. Corporate branding: an interdisciplinary literature review. Eur. J. Market. 2012; 46 (5):733–753. [ Google Scholar ]
• Guechtouli W., Rouchier J., Orillard M. Structuring knowledge transfer from experts to newcomers. J. Knowl. Manag. 2013; 17 (1):47–68. [ Google Scholar ]
• Hafner-Burton E.M., Kahler M., Montgomery A.H. Network analysis for international relations. Int. Organ. 2009; 63 (3):559–592. [ Google Scholar ]
• Hirsch J.E. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. Unit. States Am. 2005; 102 (46):16569–16572. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hirsch J.E. Does the h index have predictive power? Proc. Natl. Acad. Sci. Unit. States Am. 2007; 104 (49):19193–19198. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jing S., Qinghua Z., Landström H. Handbook of Research on Global Competitive Advantage through Innovation and Entrepreneurship. 2015. Entrepreneurship across regions: internationalization and/or contextualization? pp. 372–392. [ Google Scholar ]
• Jun S., Sung Park S. Examining technological innovation of Apple using patent analysis. Ind. Manag. Data Syst. 2013; 113 (6):890–907. [ Google Scholar ]
• Kim H., Song J. Social network analysis of patent infringement lawsuits. Technol. Forecast. Soc. Change. 2013; 80 (5):944–955. [ Google Scholar ]
• Kim Y., Choi T.Y., Yan T., Dooley K. Structural investigation of supply networks: a social network analysis approach. J. Oper. Manag. 2011; 29 (3):194–211. [ Google Scholar ]
• Korom P. International Encyclopedia of the Social & Behavioral Sciences. Vol. 16. 2015. Network analysis, history of; pp. 524–531. Second Edi. [ Google Scholar ]
• Kurt Y., Kurt M. Social network analysis in international business research: an assessment of the current state of play and future research directions. Int. Bus. Rev. 2020; 29 (2):101633. [ Google Scholar ]
• Lee P.-C., Su H.-N., Wu F.-S. Quantitative mapping of patented technology — the case of electrical conducting polymer nanocomposite. Technol. Forecast. Soc. Change. 2010; 77 (3):466–478. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Leon R.–D., Rodríguez-Rodríguez R., Gómez-Gasquet P., Mula J. Social network analysis: a tool for evaluating and predicting future knowledge flows from an insurance organization. Technol. Forecast. Soc. Change. 2017; 114 :103–118. [ Google Scholar ]
• Leung X.Y., Wang F., Wu B., Bai B., Stahura K.A., Xie Z. A social network analysis of overseas tourist movement patterns in beijing: the impact of the olympic games. Int. J. Tourism Res. 2012; 14 (5):469–484. [ Google Scholar ]
• Li C.Z., Hong J., Xue F., Shen G.Q., Xu X., Mok M.K. Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. J. Clean. Prod. 2016; 134 (Part B):482–494. [ Google Scholar ]
• Li N., Wu D.D. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis. Support Syst. 2010; 48 (2):354–368. [ Google Scholar ]
• Litterio A.M., Nantes E.A., Larrosa J.M., Gómez L.J. Marketing and social networks: a criterion for detecting opinion leaders. Eur. J. Manag. Bus. Econ. 2017; 26 (3):347–366. [ Google Scholar ]
• Liu B., Huang S., Fu H. An application of network analysis on tourist attractions: the case of Xinjiang, China. Tourism Manag. 2017; 58 :132–141. [ Google Scholar ]
• Loosemore M. Construction crises as periods of social adjustment. J. Manag. Eng. 1997; 13 (4):30–37. [ Google Scholar ]
• Monaghan S., Lavelle J., Gunnigle P. Mapping networks: exploring the utility of social network analysis in management research and practice. J. Bus. Res. 2017; 76 :136–144. [ Google Scholar ]
• Nagurney A., Dong J., Zhang D. A supply chain network equilibrium model. Transport. Res. E Logist. Transport. Rev. 2002; 38 (5):281–303. [ Google Scholar ]
• Netzer O., Feldman R., Goldenberg J., Fresko M. Mine your own business: market-structure surveillance through text mining. Market. Sci. 2012; 31 (3):521–543. [ Google Scholar ]
• Pan B., MacLaurin T., Crotts J.C. Travel blogs and the implications for destination marketing. J. Trav. Res. 2007; 46 (1):35–45. [ Google Scholar ]
• Parell C. SAGE Publication; Chennai: 2012. Social Network Analysis: History, Theory, and Methodology. [ Google Scholar ]
• Parise S. Knowledge management and human resource development: an application in social network analysis methods. Adv. Develop. Hum. Resour. 2007; 9 (3):359–383. [ Google Scholar ]
• Pesta B., Fuerst J., Kirkegaard E. Bibliometric keyword analysis across seventeen years (2000–2016) of intelligence articles. J. Intell. 2018; 6 (4):46. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pranckutė R. Web of science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications. 2021; 9 (1):12. [ Google Scholar ]
• Provan K.G., Milward H.B. Do networks really work? A framework for evaluating public-sector organizational networks. Publ. Adm. Rev. 2001; 61 (4):414–423. [ Google Scholar ]
• Pryke S.D. Analysing construction project coalitions: exploring the application of social network analysis. Construct. Manag. Econ. 2004; 22 (8):787–797. [ Google Scholar ]
• Reagans R., Zuckerman E.W. Networks, diversity, and productivity: the social capital of corporate R&D teams. Organ. Sci. 2001; 12 (4):502–517. [ Google Scholar ]
• Schröpfer V.L.M., Tah J., Kurul E. Mapping the knowledge flow in sustainable construction project teams using social network analysis. Eng. Construct. Architect. Manag. 2017; 24 (2):229–259. [ Google Scholar ]
• Sosa M.E., Eppinger S.D., Rowles C.M. The misalignment of product architecture and organizational structure in complex product development. Manag. Sci. 2004; 50 (12):1674–1689. [ Google Scholar ]
• Sozen C., Basim N., Hazir K. Social network analysis in organizational studies. Int. J. Bus. Manag. 2009; 1 (1):21–35. [ Google Scholar ]
• Stephen A.T., Toubia O. Deriving value from social commerce networks. J. Market. Res. 2010; 47 (2):215–228. [ Google Scholar ]
• Su Y.-S., Lin C.-L., Chen S.-Y., Lai C.-F. Bibliometric study of social network analysis literature. Libr. Hi Technol. 2019; 38 (2):420–433. [ Google Scholar ]
• Ting S.L., Tsang A.H.C. Using social network analysis to combat counterfeiting. Int. J. Prod. Res. 2014; 52 (15):4456–4468. [ Google Scholar ]
• Tsai W. Social structure of “coopetition” within a multiunit organization: coordination, competition, and intraorganizational knowledge sharing. Organ. Sci. 2002; 13 (2):179–190. [ Google Scholar ]
• van Eck N.J., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84 (2):523–538. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vieira E.S., Madaleno M., Azevedo G. In: Comparative Research on Earnings Management, Corporate Governance, and Economic Value. Vieira E.S., Madaleno M., Azevedo G., editors. 2021. Research on earnings management: bibliometric analysis; pp. 1–26. [ Google Scholar ]
• Vitali S., Glattfelder J.B., Battiston S. The network of global corporate control. PLoS One. 2011; 6 (10) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wellman B. Social Structures: A Network Approach. Cambridge University Press; 1988. Structural analysis: from method and metaphor to theory and substance. [ Google Scholar ]
• Wichmann B.K., Kaufmann L. Social network analysis in supply chain management research. Int. J. Phys. Distrib. Logist. Manag. 2016; 46 (8):740–762. [ Google Scholar ]
• Yang R.J., Zou P.X.W., Wang J. Modelling stakeholder-associated risk networks in green building projects. Int. J. Proj. Manag. 2016; 34 (1):66–81. [ Google Scholar ]
• Yu T., Shen G.Q., Shi Q., Lai X., Li C.Z., Xu K. Managing social risks at the housing demolition stage of urban redevelopment projects: a stakeholder-oriented study using social network analysis. Int. J. Proj. Manag. 2017; 35 (6):925–941. [ Google Scholar ]
• Zakaria R., Ahmi A., Ahmad A.H., Othman Z. Worldwide melatonin research: a bibliometric analysis of the published literature between 2015 and 2019. Chronobiol. Int. 2021; 38 (1):27–37. [ PubMed ] [ Google Scholar ]

• Original Article
• Open access
• Published: 20 October 2013

Social network analysis in innovation research: using a mixed methods approach to analyze social innovations

• Nina Kolleck 1  

European Journal of Futures Research volume  1 , Article number:  25 ( 2013 ) Cite this article

18k Accesses

27 Citations

5 Altmetric

Metrics details

The importance of social networks for innovation diffusion and processes of social change is widely recognized in many areas of practice and scientific disciplines. Social networks have the potential to influence learning processes, provide opportunities for problem-solving, and establish new ideas. Thus, they can foster synergy effects, bring together key resources such as know-how of participating actors, and promote innovation diffusion. There is wide agreement regarding the usefulness of empirical methods of Social Network Analysis (SNA) for innovation and futures research. Even so, studies that show the chances of implementing SNA in these fields are still missing. This contribution addresses the research gap by exploring the opportunities of a mixed methods SNA approach for innovation research. It introduces empirical results of the author’s own quantitative and qualitative investigations that concentrate on five different innovation networks in the field of Education for Sustainable Development.

Introduction

Scholars interested in innovation processes and futures research have often stressed the importance of social networks. Social networks are seen as an important factor in how ideas, norms, and innovations are realized. Social network research understands individuals within their social context, acknowledging the influence of relationships with others on one’s behavior. Hence, social networks can promote innovation processes and expand opportunities for learning. Despite the consensus regarding the value of social network approaches, there is a lack of empirical investigations in innovation and futures studies that use Social Network Analysis (SNA). In most cases, the scientific literature uses the concept of social networks metaphorically, ignoring the chances presented by SNA methods. At the same time, conventional empirical research in innovation and futures studies often disregards relational information. Hence, analyses of statistical data on structural and individual levels are treated as separately. Activities that are expected to have impacts on future developments are usually modeled as isolated individual or group behavior, on the one hand, or as the characteristics of structural issues, on the other hand. SNA provides us with empirical tools that capture the social context and help to better understand how innovations are implemented and diffused and why social change takes place. Network approaches explicitly challenge the difference between deduction and induction and highlight the relevance of relationships. Individuals both shape and are shaped by the social context in which they interact. By applying techniques of SNA, actor-centered and structuralist reductions are avoided. Instead, SNA emphasizes the mutual influence of structure and social connections. In order to better understand and model developments in innovation and futures research, relational data inherent to the social network perspective is needed.

This contribution addresses the opportunities of SNA for innovation research. It is divided into six sections. After this introduction , the second section briefly defines crucial concepts of SNA and provides theoretical background. The third section discusses the value of a social network perspective for innovation research. The methodological approach, along with the empirical case studies used, is outlined in the fourth section. The fifth section shows how a combination of both insights from structure based on quantitative SNA and subjective perceptions revealed with qualitative SNA is helpful for understanding innovation processes. Here, the integration of qualitative SNA such as egocentric network maps in quantitative techniques of SNA is illustrated. The contribution concludes with a summary of main arguments.

Theoretical and methodological background

While in the scientific literature there are diverse understandings on what a social network is, this contribution draws on the definition used by Stanley Wassermann and Katherine Faust:

“A social network consists of a finite set or sets of actors and the relation or relations defined on them. The presence of relational information is a critical and defining feature of a social network” [ 1 ].

This conception of social network permits both a governance approach and empirical techniques of SNA. Scholars of governance research understand social networks as a certain type of governance that can be differentiated from other ideal types of governance: markets and hierarchies. Social networks combine market-based and hierarchic dimensions and serve as a form of hybrid governance [ 2 ]. Both weak and strong modes of coordination are integrated into the network concept of governance research, where strong coordination is defined as “the spectrum of activity in which one party alters its own … strategies to accommodate the activity of others in pursuit of a similar goal” [ 3 ]. Weak coordination, on the other hand, takes place when actors observe each other’s behavior, “and then alter their actions to make their … strategies complementary with respect to a common goal” [ 3 ].

Because they promote constant exchange and deliberation, social networks have strong potential to promote ideological or structural changes and to generate new knowledge. Hence, network governance is not reduced to governmental action, but refers to the search for collective and participative problem-solving strategies and the promotion of innovations. Footnote 1 This article uses the concept of network governance to highlight the relevance of relationships for innovation research. Hence, it confronts the assumption that individual behavior is independent of any others, but instead conceives “problem-solving as a collaborative effort in which a network of actors, including both state and non-state organizations, play a part” [ 4 ]. Footnote 2

In order to better understand the opportunities of SNA for innovation research, this contribution introduces innovation networks in five different regions as case studies. Innovation networks are understood as social networks that aim at establishing a social innovation. Here, the social innovation of Education for Sustainable Development (ESD) is used. At the same time, the term social innovation refers to processes of implementing and diffusing new social concepts across different sectors of society. While “innovation” implies a kind of renewal, “social” connotes interaction of actors. Social innovations have a direct connection with the search for solutions to social problems and challenges [ 6 , 7 ]. Likewise, Education for Sustainable Development can be defined as education that empowers people to foresee, try to understand, and solve the problems that threaten life on our planet. With the goal of promoting behavioral changes that will shape a more sustainable future, ESD integrates principles of sustainable development into all aspects of education and learning.

Change and innovation through social relations

How can social networks evoke changes and what are the opportunities for SNA to promote innovation processes? SNA has the potential to overcome uncertainties related to innovation processes. The chance of an innovation gaining acceptance increase significantly if it is supported by interconnected actors rather than singular individuals. Social networks foster change processes and promote innovation diffusion. SNA techniques thus help to understand existing networks and to identify innovation potentials in order to generate new information and reveal options for structural developments. SNA has the capacity to promote innovation processes by dealing with the following issues:

Identification of innovation networks (existing, missing, possible, and realistic cooperation) and investigation of actors, structures, and network boundaries:

By using SNA methods, network structures were determined in previously defined fields. Thus, techniques of egocentric SNA provide us with necessary information with respect to network membership and structural interconnections between actors. Structural properties detected in the context of this project are, for example, centrality, prestige, or weak and strong ties.

Innovation potentials through network development strategies:

Looking at network structures not only fosters the development and diffusion of new ideas. It can also reveal where and how structural conditions enable innovations and development processes. Furthermore, Social Network Analyses disclose where and how cooperation can be optimized and where and how alterations are possible and reasonable. Presenting stakeholders the results of SNA can foster structural changes.

Identification and promotion of coordination, information, and motivation:

Analysis of social networks provides us with useful insights into knowledge transfer processes, showing where they exist and how “well” they function. Also, problems of coordination, information, and motivation become evident, providing us with knowledge related to development potentials.

Development of strategies to reduce uncertainties related to innovation processes:

The costs of information exchange are not only material (money, time), but also social. Uncertainties, lack of confidence, and the fear of a loss of reputation can prevent actors from sharing information and knowledge. Results of SNA help us to identify weaknesses in the knowledge transfer process.

Social network analysis in innovation research

In order to illustrate the key opportunities of SNA in innovation research, this section draws on the author’s own empirical investigations that used a mixed methods approach based on quantitative and qualitative SNA. Data on network members was drawn from five different German municipalities and included initiatives, institutions, thematic groups, and individuals engaged in the field of ESD. The municipalities studied are Alheim, Erfurt, Frankfurt am Main, Gelsenkirchen, and Minden. These municipalities have been awarded by the United Nations Decade of Education for Sustainable Development (UNDESD), 2005–2014, and are characterized by active networks in the field of the social innovation of ESD. Organizations, initiatives, and actors from different sectors of non-formal, informal, and formal education seek to further establish and diffuse the concept of ESD worldwide. Thus, networks within these municipalities can be regarded as best practices concerning their performance in the area of ESD. It should be taken into account, however, that the social networks analyzed here are neither institutionalized nor formally established organizations. Instead, every person engaged in the field of the social innovation is regarded as part of the network to be analyzed. Hence, defining the network boundaries was an important part of the empirical investigation.

The research design included three main steps. First, qualitative data was collected in order to gain a better understanding of the object of research and generate research hypotheses. Second, quantitative SNA was conducted, using both egocentric SNA and complete SNA techniques. Network membership and network boundaries were defined by mixed-mode egocentric SNA. In a first step, a 12-page questionnaire was sent to all persons in each of the five municipalities listed in the data base of the UNDESD. In a second step, all persons from different sectors named more than once were also approached with the questionnaire [ 8 – 10 ]. Referring to Fischer [ 11 ] and Burt [ 12 , 13 ], a name generator was used which allowed to name all relevant persons in the field of ESD. In this way, nodes were only included if they were mentioned more than once by an interviewee in the field of ESD.

The questionnaire first asked respondents to mark people in their ESD network, defined by efforts to contact, cooperation, collaboration, problem-solving, and idea exchange. Respondents were also asked to assess the quality and contact frequency for each relation mentioned and to name those persons with whom the interviewee cooperated especially closely or had established high levels of trust. They were then requested to score their named connections’ impact and the relevance with respect to the diffusion of information and the implementation of ESD. Finally, the questionnaire included questions on future prospects, desires, and developmental possibilities.

Egocentric network data was aggregated in order to enable applications of complete SNA. The (strictly adjusted) dataset of the whole network of all five municipalities consists of 1,306 persons and 2,195 edges. Subsequent to the quantitative studies, qualitative network maps were created in order to gain deeper insights into the qualitative characteristics of the networks’ structural properties. Footnote 3 This article focuses mainly on results from the second and the third part of the data analysis.

Insights from structures and individuals: engaging top-down and bottom-up approaches

Empirical results were visualized drawing back on UCINET, Netdraw, and Pajek in order to provide a comprehensive foundation for stakeholders [ 14 , 15 ]. Top-down visualizations of network data were used to generate courses of action, guidance, and network management strategies with the persons involved in the process. Thus, network visualizations and empirical insights enabled stakeholders to detect weaknesses related to structural issues, information flows, and communication problems.

In order to visualize the networks, directional relations between network members were entered into UCINET and mapped with Netdraw. The iterative method of “spring embedding” was chosen for the graph-theoretic layout, because it supports neat illustrations of data sets. Thus, the lengths of the ties do not have information content. The nodes in network visualizations represent persons engaged in implementing ESD in their municipalities. Against the backdrop of the definition of network boundaries, persons that are represented by nodes with only one ingoing link and no outgoing link were not interviewed.

To give an example, one surprising result was the low level of cooperation beyond municipal borders, as measured by network connections, as seen in Fig.  1 .

Trans-regional ESD network, generated with the graph theoretical layout spring embedding, source: Author’s data

In contrast to Manuel Castells [ 16 ], who observed a diminishing relevance of space due to the information age, the present study finds that space remains a constraint for diffusion of ESD. It seems much easier to establish the social innovation ESD in the local context with dense network structures and to subsequently foster its diffusion through weak ties [ 17 , 18 ].

Furthermore, municipal stakeholders were confronted with the unexpected existence of many structural holes and brokerage positions. The concepts “brokerage” and “structural hole” refer to actors’ structural embeddedness. A person who maintains connections with people, who do themselves not become interconnected, has the ability to mediate between these contacts and to obtain benefits from his brokerage position [ 19 – 21 ]. At the same time, structural holes impede innovation processes and information flows.

Figures  2 and 3 take Erfurt and Gelsenkirchen as examples and show relations regarding to the question of who is contacted to develop new ideas related to ESD. Only those relationships with a contact frequency of at least once a month are represented in this figure. The ESD network of Erfurt is chosen as an extreme example, because the structure of its social network exhibits the highest number of structural holes.

Cooperation in the development of new ideas in Erfurt, source: Author’s data

Cooperation in the development of new ideas in Gelsenkirchen, source: Author’s data

There are only a few network members engaged in developing new ideas with respect to ESD in Erfurt; many structural holes shape the ESD network.

In contrast, cooperation in the development of new ideas related to ESD works very well in Gelsenkirchen, as seen in Fig.  3 .

Figure  3 presents productive relationships in Gelsenkirchen. Gelsenkirchen was chosen as an example here because it demonstrates a nearly perfect cooperation basis, which is very supportive for successful innovation processes. Such results can be used by involved actors in order to disclose strengths and weaknesses and reveal where and how structural conditions enable innovations and development processes.

The network visualizations shown so far are mainly reduced to structural information. Network visualizations can also integrate further actor-related information. Not least, structural characteristics of social networks, processes of innovation, idea exchange, and trust also depend on the areas of activity to which network members belong. Thus, Fig.  4 integrates some actor-specific information. Nodes represent those people who are actively engaged in the field of ESD in Alheim. The color of the nodes indicates the sector in which the relevant person deals with ESD. The size of the nodes correlates with the individual centrality index. Centrality is measured by the frequency of the responses—the indegree [ 1 , 22 ]. The more often a person was identified by others, the more central she appears in the picture. The thickness of the connections varies depending on its individual clustering value. While there are two different measures (global and local) for clustering, the local version was used to give an indication of the embeddedness of single nodes [ 23 ]. Thus, clustering is defined as the number of common acquaintances; the thickness of the arrow connecting two nodes points to the number of triangular connections.

ESD network in Alheim; color of the nodes according to the area of activity ( blue black : non-formal education, red : administration/policy, yellow : NGOs, green : economy, light blue : formal education, orange : church, grey : other areas), numbers indicate the IDs of individuals, illustration in cooperation with fas.research, source: Author’s data

Figure  4 indicates the central role that people in the field of non-formal education play in Alheim, as measured by how often they were named by other people. Another central position is held by someone in government. The big red node has many incoming and outgoing links, but few triangle relations and thus a low clustering value. Further comparative quantitative studies reveal that despite its high density value, there is little clustering in Alheim. Certainly, the clustering value always depends on the data collection process, but as the study for this article has used the same methodological approach for all five municipal networks, it is possible to compare the municipal clustering values. However, the low clustering value in Alheim is because cooperation beyond institutional borders works very well in this municipality and persons are not always connected to the same partners. The fear expressed by other municipalities, that ESD in Alheim would be dominated by powerful politicians, cannot be confirmed from these results.

In general, quantitative SNA is able to highlight network boundaries and structural characteristics of social networks that are important to understand innovation potential and impediments. It is difficult or even impossible, however, to reveal the causes, motivations, ideas, or perceptions that lie behind such network structures by solely drawing back on quantitative SNA. How, for example, can we explain the central role of one politician in Alheim, while there are many other central persons from non-formal education? What role does this central politician play for the clustering value in Alheim? In order to answer these questions, the study had to draw on further qualitative social network research methods. The researchers thus used a combination of egocentric network maps and semi-structured interviews.

Egocentric network maps are more individual-oriented than quantitative SNA methods. One benefit of network map visualizations lies in their potential for mental or cognitive support. Such visualizations are able to promote subjective validations of interview narratives as well as to highlight subjective perceptions, reasons, motivations, and network dynamics. The technique of structured and standardized network maps, which has often been described as the “method of concentric circles” [ 24 ], was chosen for this study [ 25 ]. Here, network maps are not only aids, but a main purpose of the survey. A sheet with four concentric circles is given to the interviewee. The inner circle represents the ego, that is to say the interviewee. Interviewees are then asked to draw the initials of people important to them personally, differentiated by the degree of emotional proximity or contact frequency. The three circles around the ego represent the emotional closeness or formal distance with respect to her or his alters (or connections). The closer to the ego, the tighter a contact person is perceived by the interviewee. In addition, the circles are divided in parts through lines; each part represents a different area of activity. In this way, interviewees can dedicate their contacts to specific areas of activity, such as civil society, formal education, non-formal education, business or government. The space around ego is structured by both concentric circles that illustrate the closeness of the alters to ego and the area of activity in which alters are engaged for ESD. An essential advantage of the structured and standardized instruments in relation to unstructured techniques lies in the comparability between different network cards (both intrapersonal and interpersonal).

At the same time, the high degree of structuring and standardization constrains the significance of the data obtained. Indications beyond the pre-fixed circles are only possible if interviewers get the opportunity to pose further questions or if interviewees are encouraged to further discuss issues that are not explicitly part of the visualization process. In order to combine both standardization and openness, this study enabled the interviewers to pose further important questions and explore relevant information related to the research aims. The application of egocentric network maps also served as a medium through which interviewees talked about their relationships. In this sense, network maps were integrated into semi-structured interviews in order to generate narratives and disclose relevant relationships and action orientation. In addition, interviewees had the opportunity to choose the categories representing different areas of activity as well as the colors for the visualizations. Thus, the technique implemented in the study supported the comparability of the cases, but it was also open for new variables and dimensions related to the specific context.

Altogether 25 network maps and interviews, five in every municipality, were generated. Interviewees were chosen according to their area of activity (to obtain a variance of the cases), their position within the social network, and their centrality indexes. To give an example, Fig.  5 presents the network map of a central politician in Alheim. This network map of Alheim is also chosen to further illustrate the case of Alheim, which was also depicted in Fig.  4 . Furthermore, this ESD actor in Alheim possesses a high centrality value according to quantitative SNA.

Network Map of a central politician in Alheim, anonymized, source: Author’s data

As Fig.  5 shows, the interviewee mainly distinguishes five areas of activity: civil society, educational institutions, government/administration, business, and persons from trans-regional contexts. In some cases, the politician just wrote down an organization. During the interview, he referred to concrete persons from these organizations. Surprisingly, the sector of government/administration, to which the interviewee himself belongs, is empty: no persons or organizations are indicated. This is also reflected in the visualization based on quantitative network data (Fig.  4 ), where only one individual from government plays a central role. In a sense, qualitative studies validate quantitative results by showing that the social innovation ESD in Alheim is mainly implemented by actors from non-formal education. At the same time, qualitative results stress that the topic is supported and disseminated by one central politician who bridges structural holes between different sectors. Furthermore, school actors are not represented in the network map, whereas the closest contact persons are from civil society, educational institutions, and business. The great variety of close contact persons from different sectors can be regarded as one reason for the success of the social innovation in Alheim. The central politician in Alheim himself mentions this as playing a significant role. Further actors within the community stress that the ideological foundation and the adoption of ESD would not be possible without this politician. Hence, the establishment of ESD in Alheim can also be traced back to its structural and discursive power and the general trust of ESD actors in this well-connected politician.

The central role of the interviewee in Alheim can be ascribed to the fact that he bridges institutional clusters, supports cooperation beyond government/administration, and combines close cooperation with weak ties in the field of ESD. Furthermore, centrality is not reduced to one person or one sector. Instead, actors from different sectors play a central role in the field of ESD and cooperation between state and non-state actors is very high. In this way, it was possible to develop and realize aims in the area of political accountability in a short space of time. The dense network structure, supported by strong relations between one central politician and actors from other sectors, resulted in the elaboration of an innovative educational plan, composed according to the principles of ESD. At the same time, future strategies should focus on integrating actors from other important areas such as schools. In addition, strategies that foster trans-regional cooperation would be helpful with the diffusion of ESD.

With respect to some of the municipalities, a future strategy that fosters greater participation of stakeholders from other areas of activity, as required by the UN’s International Implementation Scheme (IIS) and the National Action Plan of the UN Decade may be helpful in promoting the implementation and diffusion of the social innovation ESD. Business actors and teachers, in particular, complain about not being sufficiently integrated into ESD networks and that the same people always take control and create turf wars. Furthermore, a lack of transparency and information exchange on existing ESD projects was seen. Business actors in these municipalities faced biases from other actors concerned that they ignored ecological and social dimensions of sustainable development. In some municipalities, ESD is mainly concentrated on environmental topics and many ESD actors express reservations about business aims. However, if different sectors are not integrated, it’s difficult to achieve a balance between ecological, economic, and social dimensions, as it has been proclaimed by the concept of sustainable development as such.

This article has explored the role of Social Network Analysis in analyzing and supporting innovation processes. In order to better understand the opportunities of SNA in innovation research, the author presented empirical results of her own quantitative and qualitative research on innovation networks in five German municipalities actively engaged in the field of ESD. The article showed the value of using a combination of both quantitative and qualitative SNA in order to better understand how and why social innovations are implemented and the opportunities to further develop the network.

Quantitative SNA was implemented to analyze the impact of structural characteristics of social networks on the implementation and the diffusion of the social innovation of ESD. It was discovered, for example, that cooperation in the field of ESD mainly takes place within municipalities and that cooperation beyond municipal borders is low and marked by structural holes. Furthermore, it was shown that social networks in the area of ESD are mostly composed of small and dense groups each representing different sets of actors (e.g., local administration, educational institutions, and business) and pursuing different interests and ideas under the umbrella of ESD. Weak ties, on the contrary, are very important in the field of ESD as they are responsible for the diffusion of innovations.

However, structural holes also exist within the municipalities with respect to the quality of the relations. The extreme example of Erfurt illustrated how the development of new ideas can be hampered by structural weaknesses. In contrast, cooperation and innovation development in the field of ESD are regarded to work well in Gelsenkirchen. In Alheim, actors from different sectors are integrated. Most central roles are played by non-formal education actors, whereas one central role is wielded by a politician. In terms of innovation diffusion, Alheim can be regarded as a best practice. Not least, cooperation beyond institutional borders works well and individual clustering values are low: persons are not always connected to the same clusters. Finally, the implementation of ESD in Alheim benefits from strong relations between one well-connected political and actors from other sectors. The central politician connects different areas of activity and promotes the integration of ecological, economic, and social dimensions in terms of sustainable development.

Quantitative techniques of SNA enabled to identify innovation networks, to determine network boundaries, to define actors within the innovation network, and to investigate the network position of actors. Problems of coordination, information, and qualitative relations were discussed. At the same time, quantitative SNA was shown unable to analyze reasons, motivations, and perceptions behind network structure. These issues were then analyzed by using qualitative SNA methods, such as network maps. A combination of qualitative and quantitative SNA techniques may thus prove the most fruitful for innovation research. In order to better understand the role of social networks in the diffusion of social innovations and to generate knowledge related to innovation potential and courses of action, qualitative techniques were used to supplement the quantitative analysis. It was assumed that the costs of information exchange are not only material (money, time), but also social. Conflicts and lack of confidence between actors, for example, may prevent successful innovation diffusion. Qualitative egocentric network maps could validate quantitative results as well as disclose subjective perceptions and orientations. The central position of one politician in Alheim could thus be traced back to its discursive and structural power. Actors in Alheim have great trust in the ideological competences of the well-connected person who supports the establishment of ESD in many sectors. Visualizations with qualitative network maps support the completion of the interview situation with visual representations. Visualized networks can also serve as mental or cognitive assistance. In combination with quantitative results, however, qualitative network maps enable us to detect where and how innovations and development processes may be possible due to structural and subjective conditions. Finally, compared to conventional statistical analysis that treat structural and individual levels as separately, analyses and visualizations of network data give us more information about the influence of social relations. SNA enables us to capture the interaction between actors and social context, to better understand how innovations are implemented and diffused, to analyze how and why social or educational change takes place or does not take place, and to disclose opportunities for future strategies.

This contribution has shown that SNA can begin to answer questions related to innovation processes. I hope it will open new avenues for further uses of SNA in innovation and futures research.

At the same time, there is little research on the democratic implications of network governance [ 5 ] as well as on the strengths and limits of the concept related to issues of educational innovations such as Education for Sustainable Development (ESD).

When examined through the framework of Social Network Analysis (SNA), the deficits of the concept of ‘Educational Governance’ become evident. In the scientific literature and in educational and political praxis, the concept of Educational Governance is often exclusively related to institutions of formal learning such as schools or educational training. In this manner, it is not possible to capture the real boundaries of social networks and to conceptualize social networks as can be done with SNA techniques. Furthermore, many actors, initiatives, and activities that play an important role in learning processes are analytically excluded in current applications of Educational Governance. For that reason, this article does not use an Educational Governance approach. Instead, it uses a governance approach that draws on theoretical concepts developed in social science.

Qualitative network maps were gathered in cooperation with a research project coordinated by Inka Bormann.

Wasserman S, Faust K (2009) Social network analysis: Methods and applications. Cambridge University Press, Cambridge

Google Scholar  

Wald A, Jansen D (2007) Netzwerke. In: Benz A, Lütz S, Schimank U, Simonis G (eds) Handbuch Governance. Theoretische Grundlagen und empirische Anwendungsbereich. VS Verlag für Sozialwissenschaften, Wiesbaden, pp 93–106

Zafonte M, Sabatier P (1998) Shared beliefs and imposed interdependencies as determinants of ally networks in overlapping subsystems. J Theo Pol 10(4):473–505

Article   Google Scholar  

Hajer M (2009) Authoritative Governance: Policy-making in the Age of Mediatization. Oxford University Press, Oxford

Book   Google Scholar  

Sørensen E, Torfing J (2005) Network governance and post-liberal democracy. Admin Theory Praxis J 27(2):197–237

Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York

Zapf W (1989) Über soziale innovationen. Soziale Welt 40(1/2):170–183

Straus F (2002) Netzwerkanalysen: gemeindepsychologische Perspektiven füür Forschung und Praxis. DUV, Wiesbaden

Wolf C (2010) Egozentrierte Netzwerke: Datenerhebung und Datenanalyse. In: Stegbauer C, Häußling R (eds) Handbuch Netzwerkforschung. VS Verlag für Sozialwissenschaften, Wiesbaden, pp 471–483

Chapter   Google Scholar  

Kolleck N (forthcoming) Qualität, Netzwerke und Vertrauen - Der Einsatz von Netzwerkanalysen in Qualitätsentwicklungsprozessen. Zeitschrift für Erziehungswissenschaft

Fischer C (1982) To Dwell among friends. Personal networks in Town and City. The University Press of Chicago, Chicago

Burt R (1982) Toward a structural theory of action: Network models of a social structure, perceptions and action. Academic, New York

Hennig M (2006) Individuen und ihre sozialen Beziehungen. VS Verlag für Sozialwissenschaften, Wiesbaden

Freeman LC (2000) Visualizing social networks. J Soc Struct 1 1. http://www.cmu.edu/joss/content/articles/volume1/Freeman.html . Accessed 09 July 2013

Krempel L (2005) Visualisierung komplexer Strukturen: Grundlagen der Darstellung mehrdimensionaler Netzwerke. Campus Verlag, Frankfurt

Castells M (2009) The rise of the network society: The information age: Economy, society, and culture, 2nd edn. Oxford, Wiley-Blackwell

Granovetter MS (1973) The strength of weak ties. Am J Soc 78(6):1360–1380

Granovetter MS (1985) Economic action and social structure: the problem of Embeddedness. Am J Soc 91(3):481–510

Burt R (1992) Structural holes. Harvard University Press, Cambridge

Burt R (2004) Structural holes and good ideas. Am J Soc 110(2):349–399

Podolny JM, Baron JN (1997) Resources and relationships: social networks and mobility in the workplace. Am Soc Rev 62(5):673–693

Freeman LC (1979) Centrality in social networks: conceptual clarification. Soc Net 1(3):215–239

Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

Hollstein B, Pfeffer J (2010) Netzwerkkarten als instrument zur Erhebung egozentierter Netzwerke. http://www.wiso.uni-hamburg.de/fileadmin/sozialoekonomie/hollstein/Team/Hollstein_Betina/Literatur_Betina/Netzwerkkarten_Hollstein_Pfeffer_2010.pdf . Accessed 09 July 2013

Kahn RL, Antonucci TC (1980) Convoys over the life course. Attachment, roles, and social support. In: Baltes PB, Brim OG (eds) Life pan development and behaviour. Academic, New York, pp 253–286

Download references

Author information

Authors and affiliations.

Universität Heidelberg & Hertie School of Governance, Friedrichstraße 180, 10117, Berlin, Germany

Nina Kolleck

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Nina Kolleck .

Additional information

I thank the editors and two anonymous reviewers for their constructive comments, which helped me to improve the article. The article is based on results of a study I conducted at the Freie Universität Berlin. I would also like to thank Gerhard de Haan for useful information and for supporting my research.

Rights and permissions

This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Reprints and permissions

About this article

Cite this article.

Kolleck, N. Social network analysis in innovation research: using a mixed methods approach to analyze social innovations. Eur J Futures Res 1 , 25 (2013). https://doi.org/10.1007/s40309-013-0025-2

Download citation

Received : 29 July 2013

Accepted : 24 September 2013

Published : 20 October 2013

DOI : https://doi.org/10.1007/s40309-013-0025-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social network analysis
• Social innovations
• Education for sustainable development
• Egocentric network maps

A Social Network Analysis of Chronic Violent Offenders

• Published: 03 June 2024

Cite this article

• Davis Shelfer 1 &
• Yan Zhang 1  

Explore all metrics

Research indicates that a majority of serious crime events are committed by a small proportion of repeat offenders. Many chronic offenders collaborate with others, co-offending on an irregular basis or as part of an organized gang. Understanding the characteristics of these offenders and their criminality has significant implications for our understanding of chronic violence and the implementation of successful, evidence-based crime prevention efforts. To contribute to this ongoing effort, we apply social network analysis (SNA) to a sample of 2,217 people arrested more than once for a violent crime between 2014 and 2022. We explore co-offending relationships, investigating differences in demographics and crime characteristics between networked and non-networked chronic violent offenders. The results of this exploratory study indicate significant differences in age and crime type by network status. This analysis also indicates that SNA is an effective method for exploring co-offending in a general-purpose crime dataset. Implications for policymakers and future directions for research are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Targeting the Most Harmful Co-Offenders in Denmark: a Social Network Analysis Approach

Investigating the Dynamics of Outlaw Motorcycle Gang Co-Offending Networks: The Utility of Relational Hyper Event Models

Copresence networks: criminals in their environment

Akers, R. L., Krohn, M. D., Lanza-Kaduce, L., & Radosevich, M. (1979). Social learning and deviant behavior: a specific test of a general theory. American Sociological Review, 44 (4), 636–655. https://doi.org/10.2307/2094592

Article   Google Scholar  

Alleyne, E., & Wood, J. L. (2010). Gang involvement: Psychological and behavioral characteristics of gang members, peripheral youth, and nongang youth. Aggressive Behavior, 36 , 423–436. https://doi.org/10.1002/ab.20360

Alleyne, E., & Wood, J. L. (2013). Gang-related crime: the social, psychological, and behavioral correlates. Psychology, Crime, and Law, 19 (7), 611–627. https://doi.org/10.1080/1068316X.2012.658050

Alleyne, E., Wood, J. L., Mozova, K., & James, M. (2016). Psychological and behavioural characteristics that distinguish street gang members in custody. Legal and Criminal Psychology, 21 , 266–285. https://doi.org/10.1111/lcrp.12072

Anastasiadis, E. K., & Antoniou, I. (2024). Directed criminal networks: temporal analysis and disruption. Information, 15 (84), 1–16. https://doi.org/10.3390/info15020084

Andresen, M. A., & Felson, M. (2012). Co-offending and the diversification of crime types. International Journal of Offender Therapy and Comparative Criminology, 56 (5), 811–829. https://doi.org/10.1177/0306624X1140715

Asscher, J. J., van Vugt, E. S., Stams, G. J. J. M., Deković, M., Eichelsheim, V. I., & Yousfi, S. (2011). The relationship between juvenile psychopathic traits, delinquency and (violent) recidivism: a meta-analysis. Journal of Child Psychology and Psychiatry, 52 (11), 1134–1143. https://doi.org/10.1111/j.1469-7610.2011.02412.x

Berlusconi, G. (2022). Come at the king, you best not miss: criminal network adaptation after law enforcement targeting of key players. Global Crime, 23 (1), 44–64. https://doi.org/10.1080/17440572.2021.2012460

Bichler, G., Malm, A., & Cooper, T. (2017). Drug supply networks: A systematic review of the organizational structure of illicit drug trade. Crime Science, 6 (2), https://doi.org/10.1186/s40163-017-0063-3

Bouchard, M., & Konarski, R. (2014). Assessing the core membership of a youth gang from its co-offending network. In C. Morselli (Ed.), Crime and networks (pp. 81–91). Routledge.

Google Scholar  

Bouchard, M. (2020). Collaboration and boundaries in organized crime: A network perspective. Crime and Justice, 49. https://doi.org/10.1086/708435

Braga, A. A., Pierce, G. L., McDevitt, J., Bond, B. J., & Cronin, S. (2008). The strategic prevention of gun violence among gang-involved offenders. Justice Quarterly, 25 (1), 132–162. https://doi.org/10.1080/07418820801954613

Bright, D. A., Greenhill, C., & Levenkova, N. (2014). Dismantling criminal networks: Can node attributes play a role? In C. Morselli (Ed.), Crime and networks (pp. 148–162). Routledge.

Bright, D. A., Greenhill, C., Reynolds, M., Ritter, A., & Morselli, C. (2015). The use of actor-level attributes and centrality measures to identify key actors: a case study of an Australian drug trafficking network. Journal of Contemporary Criminal Justice, 31 (3), 262–278. https://doi.org/10.1177/1043986214553378

Bright, D., Brewer, R., & Morselli, C. (2021). Using social network analysis to study crime: navigating the challenges of criminal justice records. Social Networks, 66 , 50–64. https://doi.org/10.1016/j.socnet.2021.01.006

Chu, C. M., Daffern, M., Thomas, S., & Lim, J. Y. (2012). Violence risk and gang affiliation in youth offenders: a recidivism study. Psychology, Crime, and Law, 18 (3), 299–315. https://doi.org/10.1080/1068316X.2010.481626

Conway, K. P., & McCord, J. (2002). A longitudinal examination of the relation between co-offending with violent accomplices and violent crime. Aggressive Behavior, 28 , 97–108. https://doi.org/10.1002/ab.90011

D’Alessio, S. J., Stolzenberg, L., & Eitle, D. (2014). “Last hired, first fired”: the effect of the unemployment rate on the probability of repeat offending. American Journal of Criminal Justice, 39 , 77–93. https://doi.org/10.1007/s12103-013-9199-1

De Moor, S., Vandeviver, C., & Beken, T. V. (2018). Integrating police-recorded crime data and DNA data to study serial co-offending behavior. European Journal of Criminology, 15 (5), 632–651. https://doi.org/10.1177/1477370817749499

Englefield, A., & Ariel, B. (2017). Searching for influential actors in co-offending networks: the recruiter. International Journal of Social Science Studies, 5 (5), 24–45. https://doi.org/10.11114/ijsss.v5i5.2351

Esbensen, F., & Carson, D. C. (2012). Who are the gangsters? An examination of the age, race/ethnicity, sex, and immigration status of self-reported gang members in a seven-city study of American youth. Journal of Contemporary Criminal Justice, 28 (4), 465–481. https://doi.org/10.1177/1043986212458192

Esbensen, F., Peterson, D., Taylor, T. J., & Freng, A. (2011). Youth violence: Sex and race differences in offending, victimization, and gang membership.  Philadelphia, PA. Temple University Press.

Farrington, D. P. (2003). Key results from the first forty years of the Cambridge Study in delinquent development. In T. P. Thornberry & M. D. Krohn (Eds.), Taking stock of delinquency: An overview of findings from contemporary longitudinal studies (pp. 137–183). Kluwer.

Chapter   Google Scholar  

Faust, K., & Tita, G. E. (2019). Social networks and crime: pitfalls and promises for advancing the field. Annual Review of Criminology, 2 , 99–122. https://doi.org/10.1146/annurev-criminol-011518-024701

Fox, B. H., Perez, N., Cass, E., Baglivio, M. T., & Epps, N. (2015). Trauma changes everything: examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child Abuse and Neglect, 46 , 163–173. https://doi.org/10.1016/j.chiabu.2015.01.011

Haberman, C. P., Sorg, E. T., & Ratcliffe, J. H. (2017). Assessing the validity of the law of crime concentration across different temporal scales. Journal of Quantitative Criminology, 33 , 547–567. https://doi.org/10.1007/s10940-016-9327-4

Hashimi, S., & Bouchard, M. (2016). On to the next one? Using social network data to inform police target prioritization. Policing: An International Journal of Police Strategies and Management, 40 (4), 768–782. https://doi.org/10.1108/PIJPSM-06-2016-0079

Jennings, W. G. (2006). Revisiting prediction models in policing: Identifying high-risk offenders. American Journal of Criminal Justice, 31 (1), 35–50. https://doi.org/10.1007/BF02885683

Katsiyannis, A., Whitford, D. K., Zhang, D., & Gage, N. A. (2018). Adult recidivism in the United States: a meta-analysis 1994–2015. Journal of Child and Family Studies, 27 , 686–696. https://doi.org/10.1007/s10826-017-0945-8

Klofas, J., & Hipple, N. K. (2006). Project Safe Neighborhoods: Strategic interventions, Crime incident reviews. [PSN Case Study #3]. United States Department of Justice. https://psn.cj.msu.edu/tta/PSN_CaseStudy3.pdf . Accessed 1 Jun 2023.

Lantz, B., & Hutchison, R. (2015). Co-offender ties and the criminal career: the relationship between co-offender group structure and the individual offender. Journal of Research in Crime and Delinquency, 52 (5), 658–690. https://doi.org/10.1177/0022427815576754

Levin, A., Rosenfeld, R., & Deckard, M. (2017). The law of crime concentration: an application and recommendations for future research. Journal of Quantitative Criminology, 33 , 635–647. https://doi.org/10.1007/s10940-016-9332-7

Malm, A., & Bichler, G. (2011). Networks of collaborating criminals: assessing the structural vulnerability of drug markets. Journal of Research in Crime and Delinquency, 48 (2), 271–297. https://doi.org/10.1177/0022427810391535

Malm, A., Bichler, G., & Nash, R. (2011). Co-offending between criminal enterprise groups. Global Crime, 12 (2), 112–128. https://doi.org/10.1080/17440572.2011.567832

McGloin, J. M. (2005). Policy and intervention considerations of a network analysis of street gangs. Criminology and Public Policy, 4 (3), 607–636.

McGloin, J. M., & Piquero, A. R. (2009). ‘I wasn’t alone’: collective behaviour and violent delinquency. The Australian and New Zealand Journal of Criminology, 42 (3), 336–353.

Moffitt, T. E. (1993). Adolescence-limited and life-course persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100 (4), 674–701.

Moffitt, T. E. (2008). A review of research on the taxonomy of life-course persistent versus adolescence-limited antisocial behavior. In F. T. Cullen, J. P. Wright, & K. R. Blevins (Eds.), Taking stock: The status of criminological theory. (vol. 15, pp. 277–311). New Brunswick, NJ. Transaction Publishers.

Morselli, C. (2010). Assessing vulnerable and strategic positions in a criminal network. Journal of Contemporary Criminal Justice, 26 (4), 382–392. https://doi.org/10.1177/1043986210377105

Morselli, C., & Petit, K. (2007). Law enforcement disruption of a drug importation network. Global Crime, 8 , 109–130. https://doi.org/10.1080/17440570701362208

Morselli, C., Giguère, C., & Petit, K. (2007). The efficiency/security trade-off in criminal networks. Social Networks, 29 , 143–153. https://doi.org/10.1016/j.socnet.2006.05.001

Niebuhr, N., & Orrick, E. A. (2020). Impact of employment satisfaction and stress on time to recidivism. Corrections: Policy, Practice and Research, 5 (3), 170–187. https://doi.org/10.1080/23774657.2018.1441761

Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28 (6), 441–453.

Papachristos, A. V., & Wildeman, C. (2014). Network exposure and homicide victimization in an African American community. American Journal of Public Health, 104 (1), 143–150. https://doi.org/10.2105/AJPH.2013.301441

Papachristos, A. V., Braga, A. A., & Hureau, D. M. (2012). Social networks and the risk of gunshot injury. Journal of Urban Health, 89 (6), 992–1003. https://doi.org/10.1007/s11524-012-9703-9

Papachristos, A. V., Hureau, D. M., & Braga, A. A. (2013). The corner and the crew: the influence of geography and social networks on gang violence. American Sociological Review, 78 (3), 417–447. https://doi.org/10.1177/0003122413486800

Papachristos, A. V., Wildeman, C., & Roberto, E. (2015). Tragic, but not random: the social contagion of nonfatal gunshot injuries. Social Science and Medicine, 125 , 139–150. https://doi.org/10.1016/j.socscimed.2014.01.056

Papachristos, A. V. (2017). The coming of a networked criminology? In J. MacDonald (Ed.), Measuring crime and criminality. (vol. 17, pp. 101–140). Routledge. https://doi.org/10.4324/9780203785997

Pyrooz, D. C., Fox, A. M., & Decker, S. H. (2010). Racial and ethnic heterogeneity, economic disadvantage, and gangs: a macro-level study of gang membership in urban America. Justice Quarterly, 27 (6), 867–892. https://doi.org/10.1080/07418820903473264

Reiss, A. J., Jr. (1980). Understanding changes in crime rates. In S. E. Fienberg & A. J. Reiss Jr. (Eds.), Indicators of crime and criminal justice: Quantitative studies (pp. 11–17). [NCJ #62349] Bureau of Justice Statistics. https://www.ojp.gov/pdffiles1/bjs/62349.pdf . Accessed 14 Mar 2024.

Reiss, A. J., Jr. (1988). Co-offending and criminal careers. In N. Morris & M. Tonry (Eds.), Crime and justice (vol. 10, pp. 117–170). University of Chicago Press.

Sparrow, M. K. (1991). The application of network analysis to criminal intelligence: an assessment of the prospects. Social Networks, 13 , 251–274. https://doi.org/10.1016/0378-8733(91)90008-H

Sutherland, E. H., Cressey, D. R., & Luckenbill, D. F. (1992). Principles of criminology (11 th ed.). Rowman & Littlefield. Lanham, MD.

Texas Penal Code, 3.12(a) Penal Code §§ 12.42, 12.425, 12.43 (2024). https://statutes.capitol.texas.gov/Docs/PE/htm/PE.12.htm . Accessed 21 Mar 2024.

Thornberry, T. P., Krohn, M. D., Lizotte, A. J., Smith, C. A., & Tobin, K. (2002). Gangs and delinquency in developmental perspective. Cambridge University Press . https://doi.org/10.1017/CBO9780511499517

Toth, S. L., & Cicchetti, D. (2013). A developmental psychopathology perspective on child maltreatment. Child Maltreatment, 18 (3), 135–139. https://doi.org/10.1177/1077559513500380

van Mastrigt, S. B., & Farrington, D. P. (2011). Prevalence and characteristics of co-offending recruiters. Justice Quarterly, 28 (2), 325–359. https://doi.org/10.1080/07418825.2010.482535

Warr, M. (1996). Organization and instigation in delinquent groups. Criminology, 34 (1), 11–37. https://doi.org/10.1111/j.1745-9125.1996.tb01193.x

Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53 (2), 133–157. https://doi.org/10.1111/1745-9125.12070

Westlake, B. G., Bouchard, M., & Frank, R. (2012). Finding the key players in online child exploitation networks. Policy & Internet, 3 (2), 1–32. https://doi.org/10.2202/1944-2866.1126

Wolfgang, M. E., Figlio, R. M., & Sellin, T. (1972). Delinquency in a birth cohort . University of Chicago Press.

Download references

Author information

Authors and affiliations.

Department of Criminal Justice and Criminology, Sam Houston State University, Huntsville, TX, USA

Davis Shelfer & Yan Zhang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Davis Shelfer .

Ethics declarations

Disclosures.

This project was partially supported by PS-Project Safe Neighborhoods Program (South), grants 3912301, 3912302, and 3912303. A previous version of the current study was presented at the 2023 Academy of Criminal Justice Sciences (ACJS) meeting.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Shelfer, D., Zhang, Y. A Social Network Analysis of Chronic Violent Offenders. Am J Crim Just (2024). https://doi.org/10.1007/s12103-024-09768-6

Download citation

Accepted : 22 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1007/s12103-024-09768-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social network analysis
• Chronic violent offender
• Co-offending
• Crime characteristics
• Find a journal
• Publish with us
• Track your research

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Table of Contents

Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

• RACE & ETHNICITY
• POLITICAL AFFILIATION

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

• Americans’ Social Media Use  Jan. 31, 2024
• Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Google research shows the fast rise of AI-generated misinformation

Artificial intelligence has become a source of misinformation with lightning speed.

Social Sharing

From fake images of war to celebrity hoaxes, artificial intelligence technology has spawned new forms of reality-warping misinformation online. New analysis co-authored by Google researchers shows just how quickly the problem has grown.

The research, co-authored by researchers from Google, Duke University and several fact-checking and media organizations, was published in a preprint last week. The paper introduces a massive new dataset of misinformation going back to 1995 that was fact-checked by websites like Snopes.

According to the researchers, the data reveals that AI-generated images have quickly risen in prominence, becoming nearly as popular as more traditional forms of manipulation.

• Don't believe your eyes — fake photos have been a problem for a long time
• Analysis With rise of AI-generated images, distinguishing real from fake is about to get a lot harder

The work was first reported by 404 Media after being spotted by the Faked Up newsletter, and it clearly shows that "AI-generated images made up a minute proportion of content manipulations overall until early last year," the researchers wrote.

Last year saw the release of new AI image-generation tools by major players in tech, including OpenAI, Microsoft and Google itself. Now, AI-generated misinformation is "nearly as common as text and general content manipulations," the paper said.

The researchers note that the uptick in fact-checking AI images coincided with a general wave of AI hype, which may have led websites to focus on the technology. The dataset shows that fact-checking AI has slowed down in recent months, with traditional text and image manipulation seeing an increase.

The study looked at other forms of media, too, and found that video hoaxes now make up roughly 60 per cent of all fact-checked claims that include media.

That doesn't mean AI-generated misinformation has slowed down, said Sasha Luccioni, a leading AI ethics researcher at machine learning platform Hugging Face.

"Personally, I feel like this is because there are so many [examples of AI misinformation] that it's hard to keep track!" Luccioni said in an email. "I see them regularly myself, even outside of social media, in advertising, for instance."

• Explicit fake images of Taylor Swift prove laws haven't kept pace with tech, experts say
• Fake photos, but make it fashion. Why the Met Gala pics are just the beginning of AI deception

AI has been used to generate fake images of real people, with concerning effects. For example, fake nude images of Taylor Swift circulated earlier this year. 404 Media reported that the tool used to create the images was Microsoft's AI-generation software, which it licenses from ChatGPT maker OpenAI — prompting the tech giant to close a loophole allowing the images to be generated.

The technology has also fooled people in more innocuous ways. Recent fake photos showing Katy Perry attending the Met Gala in New York — in reality, she never did —  fooled observers on social media and even the star's own parents.

The rise of AI has caused headaches for social media companies and Google itself. Fake celebrity images have been featured prominently in Google image search results in the past, thanks to SEO-driven content farms. Using AI to manipulate search results is against Google's policies.

Taylor Swift deepfakes taken offline. It’s not so easy for regular people

Google spokespeople were not immediately available for comment. Previously, a spokesperson told technology news outlet Motherboard that "when we find instances where low-quality content is ranking highly, we build scalable solutions that improve the results not for just one search, but for a range of queries."

To deal with the problem of AI fakes, Google has launched such initiatives as digital waterma rking , which flags AI-generated images as fake with a mark that is invisible to the human eye. The company, along with Microsoft, Intel and Adobe, is also exploring giving creators the option to add a visible watermark to AI-generated images.

"I think if Big Tech companies collaborated on a standard of AI watermarks, that would definitely help the field as a whole at this point," Luccioni said.

ABOUT THE AUTHOR

Jordan Pearson is a Toronto-based journalist and the former executive editor of Motherboard.

Related Stories

• Go Public Facebook account takeovers are targeting people you know, turning friendship into fraud
• This article is real — but AI-generated deepfakes look damn close and are scamming people

• Introduction
• Conclusions
• Article Information

BP indicates blood pressure; ITT, intention to treat; RM, remote monitoring of BP and medication adherence; SS, remote monitoring of BP and medication adherence with feedback to a social support partner; and UC, usual care.

Trial Protocol

eTable 1. End of Study BP (Complete Cases Only, n = 206)

eTable 2. Adherence to BP Monitoring (Complete Cases Only)

eTable 3. Mean Visit Utilization During and After BP Monitoring

eTable 4. Change in Systolic BP Between Baseline and End of Study Adjusted for Baseline Systolic BP Among Phase 1 (2018) and Phase 2 (2019) Participants

eTable 5. Days Elapsed from Enrollment Through End of Study Visit

eTable 6. Self-Reported Frequency of BP Monitoring and Medication Adherence at Baseline and End of Study (Paired Cases Only, n = 209)

eTable 7. Participant Experience (Completed Patients in RM or SS Group)

eFigure. Changes in BP Distribution from Baseline to End of Study (Complete Cases Only)

Data Sharing Statement

• Navigating Remote Blood Pressure Monitoring JAMA Network Open Invited Commentary June 3, 2024 Antoinette M. Schoenthaler, EdD; Safiya Richardson, MD, MPH; Devin Mann, MD, MS

See More About

Sign up for emails based on your interests, select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Download PDF
• X Facebook More LinkedIn

Mehta SJ , Volpp KG , Troxel AB, et al. Remote Blood Pressure Monitoring With Social Support for Patients With Hypertension : A Randomized Clinical Trial . JAMA Netw Open. 2024;7(6):e2413515. doi:10.1001/jamanetworkopen.2024.13515

Manage citations:

© 2024

• Permissions

Remote Blood Pressure Monitoring With Social Support for Patients With Hypertension : A Randomized Clinical Trial

• 1 Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
• 2 Center for Health Care Innovation, University of Pennsylvania, Philadelphia
• 3 Center for Health Incentives and Behavioral Economics, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
• 4 Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia
• 5 Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York
• 6 Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
• Invited Commentary Navigating Remote Blood Pressure Monitoring Antoinette M. Schoenthaler, EdD; Safiya Richardson, MD, MPH; Devin Mann, MD, MS JAMA Network Open

Question   Can remote monitoring of blood pressure (BP) alone or with feedback to the patient’s social support partner improve BP control among patients with hypertension?

Findings   In this randomized clinical trial of 246 patients with hypertension, remote BP monitoring and remote BP monitoring with social support did not improve BP control compared with usual care.

Meaning   Findings of this trial indicate that neither remote monitoring of BP alone nor combined with social support result in improved BP control in adults with hypertension. Additional research on interventions aimed at reminding patients to take their BP medications is warranted.

Importance   Hypertension management has traditionally been based on office visits. Integrating remote monitoring into routine clinical practices and leveraging social support might improve blood pressure (BP) control.

Objective   To evaluate the effectiveness of a bidirectional text monitoring program focused on BP control and medication adherence with and without social support in adults with hypertension.

Design, Setting, and Participants   This randomized clinical trial included adults aged 18 to 75 treated at an academic family medicine practice in Philadelphia in 2018 and 2019. Patients had been seen at least twice in the prior 24 months and had at least 2 elevated BP measurements (>150/90 mm Hg or >140/90 mm Hg for patients aged 18-59 years or with diabetes or chronic kidney disease) during visits. All participants had a cell phone with text messaging, offered at least 1 support partner, and were taking maintenance medications to treat hypertension. Patients were randomized 2:2:1 to remote monitoring of BP and medication adherence (RM), remote monitoring of BP and medication adherence with feedback provided to a social support partner (SS), or usual care (UC). Data were analyzed on an intention-to-treat basis between October 14, 2019, and May 30, 2020, and were revisited from May 23 through June 2, 2023.

Interventions   The RM and SS groups received an automatic home BP monitor, 3 weekly texts requesting BP measurements, 1 weekly text inquiring about medication adherence, and a weekly text with feedback. In the SS arm, support partners received a weekly progress report. The UC group received UC through their primary care practice. Clinicians caring for the patients in the intervention groups received nudges via electronic health records to adjust medications when 3 of 10 reported BP measurements were elevated. Patients were followed up for 4 months.

Main Outcomes and Measures   The primary outcome was systolic BP at 4 months measured during the final follow-up visit. Secondary outcomes included achievement of normotension and diastolic BP.

Results   In all, 246 patients (mean [SD] age, 50.9 [11.4] years; 175 females [71.1%]; 223 Black individuals [90.7%] and 13 White individuals [5.3%]) were included in the intention-to-treat analysis: 100 patients in the RM arm, 97 in the SS arm, and 49 in the UC arm. Compared with the UC arm, there was no significant difference in systolic or diastolic BP at the 4-month follow-up visit in the RM arm (systolic BP adjusted mean difference, −5.25 [95% CI, −10.65 to 0.15] mm Hg; diastolic BP adjusted mean difference, −1.94 [95% CI, −5.14 to 1.27] mm Hg) or the SS arm (systolic BP adjusted mean difference, −0.91 [95% CI, −6.37 to 4.55] mm Hg; diastolic BP adjusted mean difference, −0.63 [95% CI, −3.77 to 2.51] mm Hg). Of the 206 patients with a final BP measurement at 4 months, BP was controlled in 49% (41 of 84) of patients in the RM arm, 31% (27 of 87) of patients in the SS arm, and 40% (14 of 35) of patients in the UC arm; these rates did not differ significantly between the intervention arms and the UC group.

Conclusions and Relevance   In this randomized clinical trial, neither remote BP monitoring nor remote BP monitoring with social support improved BP control compared with UC in adults with hypertension. Additional efforts are needed to examine whether interventions directed at helping patients remember to take their BP medications can lead to improved BP control.

Trial Registration   ClinicalTrials.gov Identifier: NCT03416283

Hypertension (HTN) affects about 30% of US adults. 1 Effective treatment that reduces long-term risk is available, but only about half of adults maintain good blood pressure (BP) control depending on the guidelines used, 2 - 5 and Black patients typically have worse outcomes. 6 , 7 Control of HTN requires diagnosis, initiation of treatment, adherence to medications, and titration of medications, traditionally delivered through face-to-face primary care visits.

New care delivery models might improve outcomes. First, substantial literature has shown the benefit of remote monitoring interventions in controlling HTN. 8 - 11 Second, text messaging has become a common form of communication and could be used to engage patients in HTN management. 12 , 13 Third, new approaches to clinical practice design that use strategies from behavioral science might make patient engagement more effective. 14 , 15 For example, providing motivational feedback to patients might overcome present bias (defined as overvaluing immediate costs or rewards compared with long-term consequences) by highlighting the benefits of taking HTN medications that may not be immediately apparent for this disease, which often has no immediate symptoms. 16 Additionally, people are influenced by the behavior of others through social accountability (defined as being influenced by connections to other individuals), 17 , 18 and an opportunity to engage a friend or family member who might serve as a witness to behavior and to whom the patient might feel accountable may yield better clinical outcomes. 19 - 21 Evidence of the benefit of a feedback partner is limited, but such benefit could be enhanced through remote monitoring and text messaging in an approach called facilitated cheerleading, in which the technology platforms helps to communicate and enhance social support. 22 - 24

Among patients with poorly controlled HTN at an academic urban family medicine practice, we evaluated the effect of monitoring BP and medication adherence via bidirectional text messaging with feedback to the patient and, if needed, the clinician. We also compared the effect of remote monitoring combined with providing feedback to a social support partner with providing remote monitoring alone.

This 3-arm randomized clinical trial compared the effectiveness of 3 different approaches to improving BP control outside of office visits. Patients were randomly assigned to 1 of 3 study arms: remote monitoring of BP and medication adherence (RM), remote monitoring of BP and medication adherence with feedback to a social support partner (SS), and usual care (UC). The trial was approved by the Institutional Review Board at the University of Pennsylvania. Patients provided informed consent prior to enrollment. The protocol and statistical analysis plan are provided in Supplement 1 . This study followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline.

We included patients aged 18 to 75 years with a diagnosis of HTN who had visited an urban primary care practice in Philadelphia at the University of Pennsylvania at least twice in the prior 2 years. Patients must have had at least 2 BP readings exceeding the Eighth Joint National Committee (JNC 8) HTN guidelines (>150/90 mm Hg or >140/90 mg/Hg for patients aged 18-59 years or with diabetes or chronic kidney disease) during that time, including at the most recent visit. 25 Initial BP measurements were obtained from the electronic health record (EHR) during office visits as recorded by routine clinical practice. To be included in the trial, patients needed to have a cell phone with text messaging capability, offer at least 1 support partner, and be taking at least 1 of the JNC 8–recommended medications for HTN. We excluded patients if they had evidence of metastatic cancer, end-stage kidney disease, congestive heart failure, dementia, or a body mass index (calculated as weight in kilograms divided by height in meters squared) of 50 or greater.

Eligibility was confirmed by study staff via manual review of automated data extracted from the EHR. All eligible patients were mailed recruitment letters and an informational brochure followed by up to 5 phone calls from study staff. Included patients completed a short survey assessing their current BP monitoring and medication adherence rates and forgetfulness about taking medication.

Patients were randomly assigned to the RM, SS, or UC arm in a 2:2:1 ratio using variably sized permuted blocks of 5 and 10. Randomization was conducted using the Way to Health platform, a software platform that facilitates and automates many aspects of study design and intervention implementation. 26 Primary care clinicians were notified by a note in the EHR when one of their patients was enrolled in the study. For patients randomly assigned to the SS arm, support partners identified during recruitment were contacted and their participation was requested in support of the patient. Enrolled support partners provided their assent for participation.

The study was conducted in 2 phases. Patients in phase 1 were enrolled between May 4 and August 3, 2018; the end-of-study visit was on December 15, 2018. A total of 151 patients were recruited in phase 1. Patients in phase 2 were enrolled between January 2 and March 27, 2019; the end-of-study visit was on August 8, 2019. A total of 100 patients were recruited in phase 2. Patients received $25 upon enrollment and an additional $50 for completing the follow-up appointment at the end of the 4-month study period. Investigators and data analysts were blinded to arm assignment, but patients and research staff were not.

Patients in both the RM and SS arms were mailed an electronic BP cuff (model BP710N; OMRON Healthcare, Inc). An adult size, extra-large cuff was provided upon patient request (Medline). Patients received 3 text prompts per week to take and submit their BP measurements, and 1 text each week asking them about their medication adherence for the past week. Patients in both of these arms also received a weekly text with feedback on their BP monitoring and medication adherence from the study staff. Additionally, in the SS arm, support partners received a weekly text update on the BP monitoring and medication adherence of their associated patient. Support partners were able to opt out of providing feedback to their associated patient. If the support partner did not opt out, the patient received a once-weekly feedback text message on behalf of their support partner either encouraging them to continue their good work or to try to do better in the following week, depending on their performance (trial protocol in Supplement 1 ). Patients in the RM and SS arms were monitored for 4 months. Patients randomly assigned to the UC control arm received UC as provided by the clinical practice, which included office visits only as scheduled through routine practice.

For phase 1 (151 patients), if at any point 3 of 10 reported BP measurements were elevated per JNC 8 guidelines, the elevated measurements and any subsequent measurements (up to 10) were sent via EHR message to the patient’s primary care physician (PCP) along with the patient’s reported medication adherence and a nudge to adjust medications (“JNC 8 guidelines suggest that hypertension medications should be adjusted and added until blood pressure is controlled.”). Measurements were reported again in this same manner if they remained elevated and at least 3 weeks elapsed between nudges.

If at any time a patient’s BP was severely elevated (systolic BP ≥180 mm Hg or diastolic BP ≥110 mm Hg), a text message was sent to the patient with instructions to recheck in 15 minutes and if the BP remained elevated, to call the clinic to discuss their measurement with a nurse or other clinician. This severely elevated BP measurement, along with information about patient-reported medication adherence, was also reported directly to the PCP via an EHR message.

Based on feedback from clinicians, this procedure was updated for phase 2 (100 patients). At this time, the study team implemented an integration between the Way to Health platform and patient EHRs, so that all patient-reported BP measurements populated into a flowsheet in the EHR, and a centrally designated team of nurses and a nurse practitioner (NP) were identified to manage patient follow-up and medication changes. On enrollment into 1 of the 2 intervention arms, in addition to notifying the PCP of the patient’s enrollment, research staff sent a special BP monitoring order to the NP, who approved and signed the order for each patient. This order electronically authorized patient-submitted BP measurements to be reviewed via flowsheet directly within the patient EHR. Nursing staff met twice weekly with the NP to review all monitoring notifications in bulk and make appropriate medication adjustments with follow-up phone calls or visits as needed. Any changes to patient medication were then routed via the EHR to the PCP.

All patients were invited to an in-person follow-up visit 4 months after enrollment, scheduled between 30 days before and 30 days after the target date. Study staff performed the follow-up BP measurement at the practice site using the same machines used for routine office visits (Welch Allyn model 4200B). Patients rested for 5 minutes prior to having their BP measured, during which time they completed a brief follow-up survey assessing end of study BP monitoring and medication adherence and their experience with the intervention as measured by a net promoter score. The net promoter score measures the likelihood of a patient to recommend a service on a score of 1 (not likely) to 10 (very likely). Values of 7 and 8 are discarded and the number of detractors (6 and below) is subtracted from the number of promoters (9 or 10) to calculate the score (range, –100 to 100). Blood pressure was measured 3 times with a 1-minute rest between each measurement. The second and third BP measurements were averaged and recorded as the final BP measurement of the study. This measurement was routed as an encounter to the nursing pool and to the PCP via the EHR as a final study closeout.

The primary outcome was the systolic BP at the 4-month visit according to the intervention arm. Secondary outcomes included achievement of normotension (blood pressure control) and diastolic BP. We also evaluated self-reported BP and medication adherence submissions during the intervention. Race and ethnicity was based on self-reported data in the EHR as Black, Hispanic, White, other (patients who self-identified as other race), or unknown.

Using the intention-to-treat approach, the primary analysis evaluated the systolic BP at the 4-month visit to the trial arm, adjusting for the initial systolic BP by including the baseline measures in the model. For patients with missing BP data, we first included BP measurements available from the EHR occurring from 90 to 150 days after the participant’s enrollment in the trial, and then conducted multiple imputation using all available baseline covariates (all 246 participants). Secondary analyses assessed achievement of BP control by trial arm using χ 2 tests and repeated the analyses for systolic BP with diastolic BP at the 4-month visit (all 246 participants). In addition, we tracked BP measurements by arm from the EHR that were obtained through UC for up to 8 months after the end of the trial. We also compared self-reported BP monitoring and medication adherence by trial arm at baseline and at the end-of-study visit (209 patients) based on patient survey responses.

Systolic and diastolic BPs were compared using multivariable linear regression, and BP control was evaluated using χ 2 tests, with a 2-sided P  < .02 considered significant. The mean percentage of expected BP measurements received for patients in the RM and SS arms was compared against the UC arm using an independent t test with a 2-sided P  < .05 considered significant. All statistical analyses were performed using R, version 4.0.3 (R Project for Statistical Computing), with multiple imputation performed using the mice package in R. 27

Assuming a systolic BP SD of 5.3 mm Hg (given variability of BP over time) and a 2-sided significance level of P  < .02 (to accommodate the 3 pairwise comparisons), the sample size of 60 patients in each intervention arm and 30 patients in the control arm provided 80% power to detect a difference in systolic BP of 3.75 mm Hg between either the RM or SS group and the UC group, and a difference in systolic BP of 3.10 mm Hg between the RM and SS arms. However, based on additional clinical information obtained after the study was initiated, we estimated an SD for systolic BP of 20 mm Hg, larger than our initial estimate. Thus, we increased our accrual target to 100 patients in each intervention arm and 50 patients in the control arm, which provides 80% power to detect a difference in systolic BP of 11 mm Hg between either the RM or SS groups and the UC group, and a difference in systolic BP of 9 mm Hg between the RM and SS arms. All analyses were conducted between October 14, 2019, and May 30, 2020, and were revisited from May 23 through June 2, 2023.

We contacted 810 eligible patients identified through automated data extraction from the EHR from April 2018 to October 2018. In all, 251 patients enrolled in the trial and were randomly assigned, with 101 patients assigned to the RM arm, 100 to the SS arm, and 50 to the UC arm ( Figure ). A total of 246 patients (mean [SD] age, 50.9 [11.4] years; 175 females [71.1%] and 71 males [28.9%]; 223 Black patients [90.7%], 1 Hispanic or Latino patient [0.4%], 13 White patients [5.3%], 6 patients [2.4%] of other races, and 5 patients [2.0%] of unknown race and ethnicity) were included in the intention-to-treat analysis: 5 of the enrolled patients were excluded (2 patients were ineligible, 1 left the practice, 1 withdrew from the trial, and 1 died of unrelated causes). A total of 151 patients were enrolled in phase 1 and 100 in phase 2: 100 patients in the RM arm, 97 in the SS arm, and 49 in the UC arm. While 213 of 246 patients (86.6%) attended the end-of-study visit, 3 were excluded from complete case analyses because their last visit was outside of the 30-day window, and 4 more were excluded because of incomplete BP data from the visit. Of the 206 patients (83.7%) with complete end of study data, we found follow-up visit completion was higher in the 2 intervention arms (84.0% in the RM arm and 89.6% in the SS arm) than in the UC arm (71.4%). Additionally, 85 patients (34.6%) had diabetes and 26 (10.6%) had chronic kidney disease ( Table 1 ). We were able to enroll 88 of 100 support partners in the SS arm.

In the primary analysis, which adjusted for baseline systolic BP, systolic BP was not significantly lower in either the RM arm (adjusted mean difference, −5.25 [95% CI, −10.65 to 0.15] mm Hg; P  = .06) or the SS arm (adjusted mean difference, −0.91 [95% CI, −6.37 to 4.55] mm Hg; P  = .74) compared with the UC arm at the end of the study ( Table 2 ). The results were similar after adjusting for age, sex, race and ethnicity, body mass index, and diabetes status ( Table 3 ), when we included only participants with complete end of study data (eTable 1 in Supplement 2 ), or when we examined diastolic BP for the different analyses ( Tables 2 and 3 ; eTable 1 in Supplement 2 ). Compared with the UC group, there was no significant difference in diastolic BP at the 4-month follow-up visit in the RM arm (diastolic BP adjusted mean difference, −1.94 [95% CI, −5.14 to 1.27] mm Hg) or the SS arm (diastolic BP adjusted mean difference, −0.63 [95% CI, −3.77 to 2.51] mm Hg).

Overall, 48.8% (41 of 84) of patients in the RM arm achieved BP control at the end of the study compared with 31.0% (27 of 87) of patients in the SS arm and 40.0% (14 of 35) of patients in the UC arm, with no statistical difference across arms ( Table 4 ). The eFigure in Supplement 2 reveals favorable shifts in systolic and diastolic BP measurements from the start to the end-of-study visit that are indistinguishable from those in in the UC group. In a post hoc analysis, we did not find any differences in change in systolic BP and proportion of patients with normotension between either intervention and control arms 12 months from the start of the trial based on any office BP measurements in the EHR between 4 and 12 months after the date of enrollment (eTable 4 in Supplement 2 ). The percentage of expected BP measurements reported was similar between the RM and SS arms (mean [SD], 76.5% [19.8%] and 77.2% [21.8%]; P  = .82) (eTable 2 in Supplement 2 ).

There were similar rates of primary care, emergency department, and hospital visits across arms (eTable 3 in Supplement 2 ). In a post hoc analysis, there was no difference in the primary outcome between phases 1 and 2 (eTable 4 in Supplement 2 ). The mean (SD) time from enrollment through the end-of-study visit (phases 1 and 2 combined) was 125.2 (7.4) days in the RM arm, 121.0 (6.9) days in the SS arm, and 129.9 (7.6) days in the control arm (eTable 5 in Supplement 2 ). At enrollment, combination medications (ie, pills containing 2 different medications) were being used by 20 of 101 patients (19.8%) in the RM arm, 20 of 100 patients (20.0%) in the SS arm, and 12 of 50 patients (24.0%) in the UC arm. Patients across arms were taking a similar number of medications at the start of the study (mean [SD], 1.6 [0.7] drugs in the RM arm, 1.7 [0.9] drugs in the SS arm, and 1.5 [0.7] drugs in the UC arm).

Across both phases, requesting an office visit with the patient for BP follow-up was the second most common action taken in response to alerts (21.7%), while the most common response to alerts was to take no action (37.9%). Medications were titrated only 17.4% of the time, in a mix between remote management and in-person visits. When we reviewed clinician actions by study arm, we found that while medication doses in both groups were titrated at similar rates, alerts from the SS arm were acted on less frequently than alerts from the RM arm (38.8% vs 30.9%).

Patients self-reported their BP monitoring frequency at baseline and at the end of the study. At baseline, all groups self-reported a median monitoring frequency of 0 during the last 14 days (IQR, 0-2 in the intervention groups and 0-0 in the control group). At the end of the trial, median (IQR) reporting frequency was 9 (4-14) days in the RM arm, 8 (6-14) days in the SS arm, and 0 (0-2) days in the UC arm. All groups self-reported being adherent with medications 14 of 14 days at both baseline and the end-of-study visit (eTable 6 in Supplement 2 ). However, fewer participants from the intervention arms compared with the UC arm reported difficulty remembering to take their BP medications at the end-of-study visit compared with baseline (eTable 6 in Supplement 2 ): 27.4% in the RM arm and 19.3% in the SS arm and 43.2% in the UC arm.

Participants in the intervention arms generally agreed that the program helped them to remember to both monitor their BP and to take their medications. Participants in the RM arm were overwhelmingly very likely to recommend the program to a friend or family member who may need it (90.5%). Overall, participants gave the program a net promotor score of 76 of 100 (eTable 7 in Supplement 2 ).

In this randomized clinical trial, we found no significant improvement in BP control in either of the remote monitoring arms compared with UC. Likewise, there was no difference in self-reported frequency of BP monitoring in the intervention groups with or without social support.

The intervention incorporated remote engagement with text messaging, the provision of home BP monitors, integration of data in the EHR, and social support to help patients improve BP control. Despite the conceptual appeal of these interventions, we found no improvement in BP control in this study. Several factors might explain the results. First, the intervention was primarily focused on patient behavior, while clinician management and prescribing may also influence BP control. 28 In phase 1, the BP alerts may have overburdened PCPs, who may not have had enough time to respond appropriately. Only in phase 2 were alerts sent to a dedicated team of nurses and NPs. Additionally, there is evidence that clinical inertia may impede dose escalation. 29

Second, there may not have been enough time in the 4-month intervention for BP control to change. Many of the PCPs were still relying on office visits for responding to BP alerts since the current payment model for primary care at this institution still relies on reimbursement for office visits, which may take weeks or months to schedule. Across both phases, requesting an office visit with the patient for BP follow-up was the second most common action taken in response to alerts (21.7%), while the most common response to alerts was to take no action (37.9%). Medications were titrated only 17.4% of the time, in a mix between remote management and in-person visits. When we reviewed clinician actions by study arm, we found that while medication doses in both groups were titrated at similar rates, alerts from the SS arm were acted on less frequently than alerts from the RM arm (38.8% vs 30.9%). 30 Nevertheless, we did not observe differences across arms when we extended our assessment of outcomes an additional 8 months after the end of study office visit.

Third, social support partners may have been able to provide social accountability but may not have known how to provide substantive support. Fourth, the study relied on an opt-in consent process, so we may have selected patients who were particularly motivated and may have improved BP with no intervention. Also, the enrollment process and final BP check for the control group may have acted as an intervention. Our control group experienced a 40% rate of controlled BP at the end of the study.

The trial has strengths in design and evaluation. This trial was conducted in close partnership with an urban primary care practice with a large proportion of Black patients, who are known to have worse outcomes in BP management. We also leveraged new technology through text messaging and automation that are being used more in practices across the country, particularly since the COVID-19 pandemic. Finally, we rigorously evaluated the utility of creating a mechanism for increased social accountability that holds promise for health care delivery but also needs to be evaluated in additional clinical care settings.

An important limitation of the study is that there may not have been sufficient power to detect smaller improvements in BP control, so we cannot make conclusions about the effectiveness or lack of effectiveness of remote monitoring. The SD of the mean systolic BP change was greater than the initial estimate but lower than what was estimated in the revised power calculation. Additionally, as this was a pragmatic design, the inclusion criteria for BP control were based on routine office visits, while the outcome ascertainment was conducted through a separate end of study research visit using the same equipment, and research staff were unblinded. Finally, there was differential follow-up for the final visit, but we conducted imputation to account for missing data.

In this randomized clinical trial of adults with hypertension, we found that remote BP monitoring did not result in a statistically significant improvement in BP control with or without social support compared with UC. Future efforts to examine whether interventions directed at helping patients remember to take their BP medications, including additional insights from behavioral science, clinical pathways for dose escalation, and workflow redesign for dedicated staff, could aid in BP control.

Accepted for Publication: March 25, 2024.

Published: June 3, 2024. doi:10.1001/jamanetworkopen.2024.13515

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Mehta SJ et al. JAMA Network Open .

Corresponding Author: Shivan J. Mehta, MD, MBA, MSHP, Perelman School of Medicine, University of Pennsylvania, 3600 Civic Center Blvd, 8W-206, Philadelphia, PA 19104 ( [email protected] ).

Author Contributions: Dr Mehta had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Mehta, Volpp, Troxel, Teel, Purcell, Asch.

Acquisition, analysis, or interpretation of data: Mehta, Troxel, Teel, Reitz, Shen, McNelis, Snider.

Drafting of the manuscript: Mehta, Reitz.

Critical review of the manuscript for important intellectual content: Volpp, Troxel, Teel, Reitz, Purcell, Shen, McNelis, Snider, Asch.

Statistical analysis: Troxel, Snider.

Obtained funding: Mehta, Volpp.

Administrative, technical, or material support: Volpp, Teel, Reitz, Purcell, Shen.

Supervision: Mehta, Volpp, Teel, Reitz.

Conflict of Interest Disclosures: Dr Mehta reported receiving a grant from the National Cancer Institute during the conduct of the study and personal fees from Guardant Health and the American Gastroenterological Association outside the submitted work. Dr Volpp reported receiving grants from the National Institutes of Health (NIH) and Penn Medicine during the conduct of the study and being a co-owner of VAL Health outside the submitted work. Dr Troxel reported receiving a grant from the NIH during the conduct of the study. Dr Asch reported receiving a grant from the NIH during the conduct of the study; receiving personal fees from Deloitte, Boehringer Ingelheim, and MARS Veterinary Group; and being a co-owner of VAL Health outside the submitted work. No other disclosures were reported.

Funding/Support: This trial was supported by grant UL1TR001878 from the National Center for Advancing Translational Science.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Advancing Translational Science or the NIH.

Data Sharing Statement: See Supplement 3 .

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts