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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.

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A Guide to Social Network Analysis and its Use Cases

• Last Updated on April 23, 2021

In today’s world of limitless connectivity, multiple devices, unlimited choices, several individual personas, there is something sublime unifying all of the above. There is an invisible thread connecting all the dots despite the digital growth happening every day. According to the Chaos Theory, something as small as the flutter of a butterfly’s wing can ultimately cause a typhoon halfway around the world.

In other words, we are a part of a network in all stages of our lives, be it a social network like friends or family, an organization network like an educational institution or workplace. The networks we are a part of also include a social media network where we connect with people across the world or even a consumer network as users of various brands. Thus, networks are all around us.

The concept of networks and extracting information has untapped potential, be it a social setting, consumer behavior, health management, education, politics. Though intellectuals have started seeing the benefits of identifying social groups for various applications, this concept has not become mainstream in the business world. This blog delves into SNA (Social Network Analysis) and how it can be used to analyze and solve business-related problems.

What is SNA?

Social Network Analysis (SNA), also known as network science, is a general study of the social network utilizing network and graph theory concepts. It explores the behavior of individuals at the micro-level, their relationships (social structure) at the macro level, and the connection between the two.

SNA uses several methods and tools to study the relationships, interactions, and communications in a network. This study is key to procedures and initiatives involving problem-solving, administration, and operations of that network.

The basic entities required for building a network are nodes and the edges connecting the nodes. Let us try and understand this with the help of a most common application of SNA, the Internet. Webpages are often linked to other web pages on their own page or other pages. In SNA language, these pages are nodes, and the links between the pages are the edges. In this way, we can interpret the entire internet as one large graph.

SNA is a commonly used approach for analyzing interpersonal connections on the internet due to the boom of social media networking. But this concept is not limited to online social networks; it can be used for any application that can be modeled as a network.

A Guide to the Most Used SNA Terminologies

As established earlier, nodes and edges are the building blocks for SNA. Few characteristics of the edges that define the features of a network are shown below.

The Edges connect the Nodes. The direction of connections determines the Edge type.

1.a Directed Edge: The nodes connected by this edge are ordered, that is, the connection between the nodes is one way. For example, Twitter, Instagram are predominantly directed edge networks. You can follow someone without them following you back.

1.b Undirected Edge: The relationship between the nodes connected by this edge is mutual, i.e., the connection is applicable both ways. E.g., Befriending a person on Facebook, LinkedIn automatically creates a two-way connection.

2. Weight: In a weighted network, an edge carries a label (weight) between the nodes. Different applications can have their own definition of weight. In social media analysis, a weight can define the number of mutual connections between the nodes connected by that edge.

In Figure 2 , John and Frank have two mutual friends, Rose and Amy. Thus, the edge connecting John and Frank carries a weight of 2.

3. Density : The relation between the number of existing connections in a network and all possible connections in the network is calculated as follows:

In Figure 3 , we have a five-point/node network. The total possible connections in this network are 10. Figure 3.a has nine edges; its density is 90%. Hence it is a high-density network. Whereas Figure 3.b has only four edges, it has a low density of 40%.

Centrality Measures:

a) Degree Centrality: Measures the number of direct ties to a node; this will indicate the most connected node in the group.

Let’s consider the network in Figure 4 . The degree centrality score of a network is the sum of edges connected to that node. For Node 1, the degree centrality is 1, and for Nodes 3 and 5, the score is 3.

The standardized score is calculated by dividing the score by (n-1), where n is the number of nodes in the network.

We can see that nodes 3 and 5 have a high degree centrality of 0.5, i.e., they are the most well-connected nodes in the network.

b) Closeness Centrality: Closeness measures how close a node is to the rest of the network. It is the ability of the node to reach the other nodes in the network. It is calculated as the inverse of the sum of the distance between a node and other nodes in the network. 

Let us take node 1 from Figure 4 ; the sum of distances from node 1 to all other nodes is 16.

Hence the Closeness score for node 1 will be 1/16. The standardized score is calculated by multiplying the score by (n-1).

We can conclude that node 4 is the closest/central node in the network with the highest closeness score of 0.6.

c) Betweenness Centrality: It is a measure of how often a node appears in the shortest path connecting two other nodes. Let us take node 5 in Figure 4 . Node 5 occurs in 9 shortest paths between a pair of nodes (as shown in Table 4 ).

If node 5 is the only node in the path, then the path value is 1. If it is one of the ‘n’ nodes in the shortest path, then the path value is 1/n. The sum of path values for node 5 for all nine pairs of nodes is its betweenness score. These values are then standardized by dividing the score by (n-1)*(n-2)/2

Nodes with high betweenness centrality are critical in controlling and maintaining flow in the network; hence these are critical nodes in the network

. d) Eigenvector Centrality: A relative measure of the importance of the node in the network. Each node is assigned a value or score depending upon the number of other prominent/ high scoring nodes it is connected to.

Why do we need such a relative measure? Consider the network in Figure 5 . Here ‘d’ represents the degree centrality score. Nodes A and B are connected to 4 nodes each, and hence both have a degree centrality score of 4. But when we look at their neighbors, we can see that node B is connected to nodes with a high degree. Hence, node B can be preferred over node A when we have to choose based on connectivity.

Real-world use cases of Social Network Analysis:

1. Supply Chain Management: A supply chain can be modeled into a network of supplier/consumer relations. Network analysis on the supply chain helps us improve the operation efficiency by identifying and eliminating less important nodes (suppliers/warehouses). It can help identify crucial nodes in the network and create a standby in crises or emergencies.

Nodes include Retailers, Suppliers, Warehouses, Transporters, Regulatory agencies.

SNA applications can help manufacturers identify more operationally critical nodes and identify potential sources to increase the number of connections to suppliers. This can also help identify any bottlenecks in the supply process and inventory management.

2. Human Resources:  HRM often strives to identify critical resources and understand their contribution to the organization flow, collaboration, participation, and information flow. By following the Organizational Network Analysis (ONA), an organization will optimize the talent connections, productivity, and utilization. 

It will also help identify the reach of an individual, identify accelerators of growth and poorly connected resources, and decide whom to give more opportunity.

3. Transmission of Infectious Diseases: SNA could help identify and isolate individuals and groups with high betweenness and out-degree centrality (transmitters of disease) and implement sound contact tracing activities to mellow the impact.

Apart from Contact tracing, SNA can also identify dominant themes and relations between keywords and identify the sentiment. Here is the connection between the top 10 words for COVID-19 themes:

4. Finance, Fraud detection : Financial organizations can use SNA for fraud detection. Fraud is often organized by groups of people loosely connected to each other. Such a network mapping will enable financial institutions to identify customers who may have relations to individuals or organizations on their criminal watchlist (network) and take precautionary measures.

SNA can also be used to deny access to potential hacking networks, identify a fraud ring, and series of money transactions that could be linked to Money Laundering activities.

As a Business Leader, you will have to make many critical decisions regarding effective employee performance, supply chain management , and eliminating bottlenecks in an operation process, contact tracking, credit risks, and several other use cases. SNA has immense potential to elevate existing analysis, given there is information flow and connections.

Get in touch with us or mail us at [email protected] to know how SNA can be applied to add value to your business.

  References:

• https://towardsdatascience.com/how-to-get-started-with-social-network-analysis-6d527685d374
• https://www.sciencedirect.com/science/article/pii/S2212017315001528
• https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-020-01119-3
• https://www.cgi.com/sites/default/files/white-papers/Implementing-social-network-analysis-for-fraud-prevention.pdf
• https://www.mphasis.com/content/dam/mphasis-com/global/en/nextlabs/resources/home/whitepapers/Social-Network-Analytics-for-Fraud-Detection.pdf

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Social network analysis in organizations.

• Jessica R. Methot , Jessica R. Methot School of Management and Labor Relations, Rutgers University; Exeter Business School, University of Exeter
• Nazifa Zaman Nazifa Zaman School of Management and Labor Relations, Rutgers University
• , and  Hanbo Shim Hanbo Shim School of Management and Labor Relations, Rutgers University
• https://doi.org/10.1093/acrefore/9780190224851.013.228
• Published online: 23 March 2022

A social network is a set of actors—that is, any discrete entity in a network, such as a person, team, organization, place, or collective social unit—and the ties connecting them—that is, some type of relationship, exchange, or interaction between actors that serves as a conduit through which resources such as information, trust, goodwill, advice, and support flow. Social network analysis (SNA) is the use of graph-theoretic and matrix algebraic techniques to study the social structure, interactions, and strategic positions of actors in social networks. As a methodological tool, SNA allows scholars to visualize and analyze webs of ties to pinpoint the composition, content, and structure of organizational networks, as well as to identify their origins and dynamics, and then link these features to actors’ attitudes and behaviors. Social network analysis is a valuable and unique lens for management research; there has been a marked shift toward the use of social network analysis to understand a host of organizational phenomena. To this end, organizational network analysis (ONA) is centered on how employees, groups, and organizations are connected and how these connections provide a quantifiable return on human capital investments. Although criticisms have traditionally been leveled against social network analysis, the foundations of network science have a rich history, and ONA has evolved into a well-established paradigm and a modern-day trend in management research and practice.

• social networks
• social network analysis
• organizational networks
• network theory
• social capital
• network dynamics
• multiplexity

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Social Network Analysis: A Survey on Process, Tools, and Application

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• Lee S Tanveer J Rahmani A Alinejad-Rokny H Khoshvaght P Zare G Malekpour Alamdari P Hosseinzadeh M (2025) SFGCN: Synergetic fusion-based graph convolutional networks approach for link prediction in social networks Information Fusion 10.1016/j.inffus.2024.102684 114 (102684) Online publication date: Feb-2025 https://doi.org/10.1016/j.inffus.2024.102684
• Kumar Meena S Sheshar Singh S Singh K (2024) Cuckoo Search Optimization-Based Influence Maximization in Dynamic Social Networks ACM Transactions on the Web 10.1145/3690644 Online publication date: 28-Aug-2024 https://dl.acm.org/doi/10.1145/3690644

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Understanding Social Network Analysis: A Complete Guide

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• Key Takeaways

Social Network Analysis (SNA) offers deep insights into interconnected relationships and network structures, aiding decision-making processes across various domains.

Understanding data privacy concerns and ethical considerations is crucial in conducting responsible SNA research and analysis.

Effectively handling big data is a significant challenge in SNA, requiring advanced tools and strategies for accurate analysis and interpretation.

Ethical guidelines and transparency are paramount in navigating the complexities of SNA research, ensuring integrity and respect for individual privacy.

Mastering SNA involves striking a balance between technical proficiency and ethical considerations, unlocking its full potential for impactful insights and applications.

Learning Social Network Analysis (SNA) reveals a world of connections and data insights. This guide will teach you its basics, methods, and uses. Yet, a key question remains: How can we fully use SNA to better understand human interactions and decisions?

Introduction To Social Network Analysis

• What is Social Network Analysis (SNA)?

Social Network Analysis (SNA) uses networks and graph theory to study social structures. It maps and measures relationships and flows among people, groups, or organizations.

This reveals interaction patterns and network structures. SNA is key in understanding information, resources, and influence flow. It offers insights beyond traditional methods.

• Importance of SNA in Modern Research

Modern research values Social Network Analysis (SNA). It uncovers social interaction dynamics and complexity. SNA is crucial in sociology, anthropology, epidemiology, and organizational studies. It reveals how relationships shape behavior.

For example, in public health, SNA tracks disease spread in communities. In business, it shows how informal networks impact effectiveness. Researchers, through SNA, gain insights into social issues. This leads to better interventions and strategies.

Key Concepts in Social Network Analysis

• Nodes and Edges

Social Network Analysis (SNA) focuses on two key elements: nodes and edges. Nodes stand for network members, like people, organizations, or computers. Meanwhile, edges are their direct connections, showing interactions. Knowledge of these elements is vital. They are the building blocks of social networks, allowing analysts to understand complex relationships.

• Types of Networks

Different types of networks are essential to grasp in Social Network Analysis. These include:

• Undirected Networks : Here, connections between nodes have no direction, indicating a mutual relationship, such as friendships.
• Directed Networks : In these networks, edges have a direction, showing a one-way relationship, like followers on Twitter.
• Weighted Networks : These networks assign weights to edges, representing the strength or frequency of the connection, such as the number of emails exchanged between individuals.
• Network Metrics

Network metrics are critical for quantifying the structure and properties of social networks. Key metrics in Social Network Analysis include:

• Degree Centrality : This measures the number of direct connections a node has, indicating its activity level within the network.
• Betweenness Centrality : This metric shows the extent to which a node lies on the shortest paths between other nodes, highlighting its role as a bridge or mediator.
• Closeness Centrality : This measures how close a node is to all other nodes in the network, reflecting its ability to spread information efficiently.

Methodologies for Social Network Analysis

• Data Collection Techniques

In Social Network Analysis (SNA), data collection plays a pivotal role in extracting meaningful insights from social networks.

One of the primary techniques used is surveying , where individuals are asked to identify their connections and relationships within a network. This approach helps in mapping out the structure and dynamics of the network.

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Another valuable technique is archival data analysis , which involves studying existing records such as communication logs, email threads, or organizational charts to uncover patterns and relationships within the network. This method provides a historical perspective and can reveal how networks evolve over time.

• Commonly Used SNA Software

Several software tools are available for conducting Social Network Analysis (SNA), each offering unique features and functionalities.

Gephi is a popular open-source tool known for its interactive visualization capabilities and extensive network analysis algorithms. It allows users to explore and analyze large-scale networks with ease.

UCINET (UCI Network) is another widely used software package that provides a comprehensive suite of tools for network analysis, including centrality measures, clustering algorithms, and statistical tests. It is favored by researchers and analysts for its robustness and versatility in handling diverse network datasets.

NodeXL stands out for its integration with Microsoft Excel, making it accessible to users familiar with spreadsheet-based data manipulation. It offers a user-friendly interface and supports various network metrics and visualizations, making it suitable for both beginners and advanced analysts.

• Visualization Techniques in SNA

Visualization is a crucial aspect of Social Network Analysis (SNA) as it allows researchers and practitioners to interpret complex network structures and patterns visually.

Node-Link Diagrams represent nodes (individual entities) and edges (relationships) in a network graphically, providing a clear depiction of connections and clusters.

Heatmaps and matrix plots are employed to visualize network data in a matrix format, highlighting the strength and density of relationships between nodes. These visualizations aid in identifying key influencers, detecting communities, and understanding the flow of information or resources within the network.

Interactive visualizations enhance the exploration and analysis process by enabling users to interactively navigate and filter network data, zoom into specific regions, and extract detailed information on nodes and edges. This dynamic approach fosters deeper insights and facilitates communication of findings to stakeholders effectively.

Applications of Social Network Analysis

• SNA in Social Media Analytics

Social Network Analysis (SNA) plays a pivotal role in understanding the dynamics of social media platforms . It helps in analyzing the relationships, interactions, and influence among individuals or entities within these digital networks.

By applying SNA techniques, businesses can gain insights into user behavior, identify key influencers, track information flow, and optimize their social media strategies for better engagement and ROI .

• SNA in Healthcare

In the realm of healthcare, Social Network Analysis (SNA) has emerged as a valuable tool for studying patient-provider relationships, healthcare collaborations, and disease transmission patterns.

By mapping out the social networks within healthcare settings, researchers and practitioners can identify central nodes, assess information dissemination, detect potential bottlenecks, and enhance care coordination for improved patient outcomes and organizational efficiency.

• SNA in Organizational Behavior

Social Network Analysis (SNA) offers profound insights into organizational behavior by examining the relationships, communication patterns, and knowledge sharing among employees, departments, and external stakeholders.

By leveraging SNA, organizations can identify informal leaders, enhance collaboration, streamline decision-making processes, foster innovation, and strengthen overall performance and productivity.

• SNA in Political Science

In the realm of political science, Social Network Analysis (SNA) provides a systematic approach to studying political actors, alliances, power dynamics, and information dissemination within political systems.

By employing SNA techniques, researchers can analyze political networks, assess influence flows, map out lobbying efforts, understand coalition formations, and gain a deeper understanding of the complex socio-political landscape for informed decision-making and policy development.

Advanced Topics in SNA

• Network Dynamics and Evolution

Social Network Analysis (SNA) delves into the dynamic nature of networks, exploring how they evolve and transform over time. This field investigates the intricate processes that drive changes within networks, encompassing both growth and decline phenomena. By studying these dynamics, analysts gain valuable insights into the underlying mechanisms that shape network structures.

Modeling Network Growth and Decline

In understanding Social Network Analysis, it’s essential to grasp the methodologies used to model network growth and decline. Researchers employ various mathematical and computational models to simulate these processes, allowing them to predict and analyze network changes over time. These models play a crucial role in forecasting network trends and anticipating potential shifts in connectivity patterns.

• Community Detection Algorithms

A fundamental aspect of Social Network Analysis involves community detection algorithms. These algorithms are designed to identify clusters and subgroups within a network, revealing distinct communities based on shared attributes or interactions. Different methods, such as modularity optimization and hierarchical clustering, are employed to uncover meaningful structures within complex networks.

Different Community Detection Methods

Social Network Analysis encompasses a range of community detection methods, each offering unique advantages and applications. From traditional approaches like hierarchical clustering to advanced techniques like spectral clustering and Louvain algorithm, analysts have a diverse toolkit to explore and analyze network communities. These methods facilitate a nuanced understanding of network dynamics and community structures.

• Social Network Analysis Tools and Software

To conduct in-depth analyses, researchers and practitioners rely on specialized Social Network Analysis tools and software. Popular packages like Gephi and NetworkX provide comprehensive functionalities for visualizing, modeling, and analyzing networks.

Additionally, online platforms and resources offer accessible tools for conducting SNA studies, enhancing collaboration and knowledge sharing within the field.

Challenges and Limitations of Social Network Analysis

• 1. Data Privacy Concerns

When delving into Social Network Analysis (SNA), one immediate challenge is navigating data privacy concerns. The intricate web of connections analyzed in SNA often involves personal information, raising questions about consent, confidentiality, and data protection.

Striking a balance between extracting valuable insights and respecting individuals’ privacy rights remains a critical consideration in SNA research and practice.

• 2. Handling Big Data

Another significant challenge in Social Network Analysis is effectively handling big data. With the exponential growth of digital interactions, SNA researchers often encounter vast amounts of data that require advanced tools and techniques for processing and analysis.

Scalability, computational resources, and data management strategies become paramount in ensuring the accuracy and reliability of SNA outcomes.

• 3. Ethical Considerations in SNA Research

Ethical considerations play a crucial role in Social Network Analysis research endeavors. Researchers must navigate ethical dilemmas concerning data collection methods, participant consent, and the potential impact of their findings on individuals and communities. Maintaining transparency, integrity, and adherence to ethical guidelines are fundamental pillars in conducting ethically sound SNA studies.

In conclusion, Social Network Analysis is a powerful tool for understanding relationships and interactions within networks. By analyzing connections, nodes, and patterns, businesses can gain valuable insights into their audience, improve decision-making, and enhance network performance. Mastering these concepts can lead to more effective strategies and meaningful outcomes in various fields.

• What is Social Network Analysis?

Social Network Analysis (SNA) is a methodology used to study relationships and interactions within a network of individuals, groups, or organizations. It involves mapping and measuring the relationships and flows between people, groups, organizations, computers, or other information/knowledge processing entities. By analyzing these networks, SNA can uncover patterns and insights that are not apparent through traditional analysis.

• Why is Social Network Analysis important?

Social Network Analysis is crucial for understanding the complex dynamics of interactions within various networks, from social media platforms to organizational structures. It helps identify key influencers, understand information flow, and detect communities or clusters. This analysis is vital for strategic decision-making in marketing, public health, organizational management, and more.

• What tools are commonly used in Social Network Analysis?

Common tools for Social Network Analysis include Gephi, UCINET, and NodeXL, which provide powerful visualization and analysis capabilities. These tools help researchers and analysts map networks, calculate network metrics, and visualize relationships. Each tool offers unique features tailored to different types of network analysis, making them essential for both beginners and experts.

• What are the key metrics used in Social Network Analysis?

Key metrics in Social Network Analysis include degree centrality, betweenness centrality, and closeness centrality. Degree centrality measures the number of direct connections an entity has, betweenness centrality indicates the entity’s role as a bridge within the network, and closeness centrality measures how quickly an entity can access others in the network. These metrics help identify influential nodes and understand the network’s structure.

• What are the ethical considerations in Social Network Analysis?

Ethical considerations in Social Network Analysis include data privacy, consent, and the potential misuse of network data. Researchers must ensure that data is collected and used responsibly, protecting individuals’ privacy and obtaining necessary permissions. It’s also important to consider the impact of network analysis findings on individuals and groups, avoiding harm or exploitation.

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Social Networks: Analysis and Case Studies

• © 2014
• Şule Gündüz-Öğüdücü 0 ,
• A. Şima Etaner-Uyar 1

Computer and Informatics Department, Istanbul Technical University, Istanbul, Turkey

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Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey

• Social Network Analysis (SNA) is a fast growing field
• Social media has become a very important communication platform with the increasing popularity of Web 2.0
• Provides a bridge between theory and applications of SNA methods

Part of the book series: Lecture Notes in Social Networks (LNSN)

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The present volume provides a comprehensive resource for practitioners and researchers alike-both those new to the field as well as those who already have some experience. The work covers Social Network Analysis theory and methods with a focus on current applications and case studies applied in various domains such as mobile networks, security, machine learning and health. With the increasing popularity of Web 2.0, social media has become a widely used communication platform. Parallel to this development, Social Network Analysis gained in importance as a research field, while opening up many opportunities in different application domains. Forming a bridge between theory and applications makes this work appealing to both academics and practitioners as well as graduate students.

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Table of contents (10 chapters)

Front matter, introduction to social networks: analysis and case studies.

• Nagehan İlhan, Şule Gündüz-Öğüdücü, A. Şima Etaner-Uyar

Ranking Authors on the Web: A Semantic AuthorRank

• Lule Ahmedi, Lavdim Halilaj, Gëzim Sejdiu, Labinot Bajraktari

Detecting Neutral Nodes in a Network of Heterogeneous Agent Based System

• Fatemeh Hendijani Fard, Behrouz H. Far

Global Structure in Social Networks with Directed Typed Edges

• David B. Skillicorn, Quan Zheng

Social Networks and Group Effectiveness: The Role of External Network Ties

• Fabiola Bertolotti, Maria Rita Tagliaventi

Overlapping Community Discovery Methods: A Survey

• Alessia Amelio, Clara Pizzuti

Classification in Social Networks

• Zehra Çataltepe, Abdullah Sönmez

Experiences Using BDS: A Crawler for Social Internetworking Scenarios

• Francesco Buccafurri, Gianluca Lax, Antonino Nocera, Domenico Ursino

Privacy and Ethical Issues in Social Network Analysis

• Lei Chen, Mingxuan Yuan

Social Media: The Evolution of e-Health Services

• Tiziana Guzzo, Alessia D’Andrea, Fernando Ferri, Patrizia Grifoni

Back Matter

“Experts in the field of social networks aim to help readers grasp the state of the art in the topic of network-based representation and analysis in this collection of papers. … the editors give readers a snapshot of recent work by presenting in each chapter a self-contained paper from different experts in the field. … recommend the book to anyone that wants to quickly get a grasp on the heterogeneity of subtopics that are studied in the social networks domain.” (Sergio Queiroz, Computing Reviews, April, 2015)

Editors and Affiliations

Şule Gündüz-Öğüdücü

A. Şima Etaner-Uyar

Bibliographic Information

Book Title : Social Networks: Analysis and Case Studies

Editors : Şule Gündüz-Öğüdücü, A. Şima Etaner-Uyar

Series Title : Lecture Notes in Social Networks

DOI : https://doi.org/10.1007/978-3-7091-1797-2

Publisher : Springer Vienna

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer-Verlag Wien 2014

Hardcover ISBN : 978-3-7091-1796-5 Published: 29 July 2014

Softcover ISBN : 978-3-7091-1990-7 Published: 23 August 2016

eBook ISBN : 978-3-7091-1797-2 Published: 11 July 2014

Series ISSN : 2190-5428

Series E-ISSN : 2190-5436

Edition Number : 1

Number of Pages : XX, 249

Number of Illustrations : 8 b/w illustrations, 41 illustrations in colour

Topics : Computer Appl. in Social and Behavioral Sciences , Applications of Graph Theory and Complex Networks , Methodology of the Social Sciences

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• 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

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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

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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.

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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

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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

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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 ]