
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks,meme proliferation, information circulation,friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships,collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

Social network analysis has emerged as a key technique in modern sociology. It has also gained significant popularity in the following: anthropology, biology,demography, communication studies,economics, geography, history, information science, organizational studies,physics,political science, public health,social psychology, development studies, sociolinguistics, and computer science, education and distance education research, and is now commonly available as a consumer tool (see the list of SNA software).
History
Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international.
In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, John Arundel Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity).
Starting in the 1970s, scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis.
Beginning in the late 1990s, social network analysis experienced a further resurgence with work by sociologists, political scientists, economists, computer scientists, and physicists such as Duncan J. Watts, Albert-László Barabási, Peter Bearman, Nicholas A. Christakis, James H. Fowler, Mark Newman, Matthew Jackson, Jon Kleinberg, and others, developing and applying new models and methods, prompted in part by the emergence of new data available about online social networks as well as "digital traces" regarding face-to-face networks.
Computational SNA has been extensively used in research on study-abroad second language acquisition. Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo, Wouter De Nooy, and Burgert Senekal. Indeed, social network analysis has found applications in various academic disciplines as well as practical contexts such as countering money laundering and terrorism.[citation needed]
Metrics
Size: The number of network members in a given network.
Connections
Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Homophily is also referred to as assortativity.
Multiplexity: The number of content-forms contained in a tie. For example, two people who are friends and also work together would have a multiplexity of 2. Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.
Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.
Network Closure: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.
Propinquity: The tendency for actors to have more ties with geographically close others.
Distributions
Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.
Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network. Examples of common methods of measuring "centrality" include betweenness centrality,closeness centrality, eigenvector centrality, alpha centrality, and degree centrality.
Density: The proportion of direct ties in a network relative to the total number possible.
Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the idea of 'six degrees of separation'.
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality). Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.
Segmentation
Groups are identified as 'cliques' if every individual is directly tied to every other individual, 'social circles' if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.
Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.
Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.
Modelling and visualization of networks
Visual representation of social networks is important to understand the network data and convey the result of the analysis. Numerous methods of visualization for data produced by social network analysis have been presented. Many of the analytic software have modules for network visualization. The data is explored by displaying nodes and ties in various layouts and attributing colors, size, and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information. Still, care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.
Signed graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating), and a negative edge denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. According to balance theory, balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, and yet C and A have a negative relationship, is an unbalanced cycle. This group is very likely to change into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concepts of balanced and unbalanced graphs, the evolution of a social network graph may be forecasted.
Different approaches to participatory network mapping have proven useful, especially when using social network analysis as a tool for facilitating change. Here, participants/interviewers provide network data by mapping the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * Net-map toolbox. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.
Social networking potential
Social Networking Potential (SNP) is a numeric coefficient, derived through algorithms to represent both the size of an individual's social network and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is Alpha User, defined as a person with a high SNP.
SNP coefficients have two primary functions:
- The classification of individuals based on their social networking potential, and
- The weighting of respondents in quantitative marketing research studies.
By calculating the SNP of respondents and by targeting High SNP respondents, the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced.
Variables used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See Viral Marketing.
The first book to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of social marketing intelligence was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco (UCL) presents at TEDx the Social Networking Potential as a parallelism to the potential energy that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".
Practical applications
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining, network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, and filtering, recommender systems development, and link prediction and entity resolution. In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, information system development analysis, marketing, and business intelligence needs (see social media analytics). Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving.
Longitudinal SNA in schools
Large numbers of researchers worldwide examine the social networks of children and adolescents. In questionnaires, they list all classmates, students in the same grade, or schoolmates, asking: "Who are your best friends?". Students may sometimes nominate as many peers as they wish; other times, the number of nominations is limited. Social network researchers have investigated similarities in friendship networks. The similarity between friends was established as far back as classical antiquity. Resemblance is an important basis for the survival of friendships. Similarity in characteristics, attitudes, or behaviors means that friends understand each other more quickly, have common interests to talk about, know better where they stand with each other, and have more trust in each other. As a result, such relationships are more stable and valuable. Moreover, looking more alike makes young people more confident and strengthens them in developing their identity. Similarity in behavior can result from two processes: selection and influence. These two processes can be distinguished using longitudinal social network analysis in the R package SIENA (Simulation Investigation for Empirical Network Analyses), developed by Tom Snijders and colleagues. Longitudinal social network analysis became mainstream after the publication of a special issue of the Journal of Research on Adolescence in 2013, edited by René Veenstra and containing 15 empirical papers.
Security applications
Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as an espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network. This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network. The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.
Textual analysis applications
Large textual corpora can be turned into networks and then analyzed using social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analyzed using tools from network theory to identify the key actors, the key communities or parties, and general properties such as the robustness or structural stability of the overall network or the centrality of certain nodes. This automates the approach introduced by Quantitative Narrative Analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text. In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).
Internet applications
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web. The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.
Netocracy
Another concept that has emerged from this connection between social network theory and the Internet is the concept of netocracy, where several authors have emerged studying the correlation between the extended use of online social networks, and changes in social power dynamics.
Social media internet applications
Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook.
In computer-supported collaborative learning
One of the most current methods of the application of SNA is to the study of computer-supported collaborative learning (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication. Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network. When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own.
Key terms
There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: density, centrality, indegree, outdegree, and sociogram.
- Density refers to the "connections" between participants. Density is defined as the number of connections a participant has, divided by the total possible connections a participant could have. For example, if there are 20 people participating, each person could potentially connect to 19 other people. A density of 100% (19/19) is the greatest density in the system. A density of 5% indicates there is only 1 of 19 possible connections.
- Centrality focuses on the behavior of individual participants within a network. It measures the extent to which an individual interacts with other individuals in the network. The more an individual connects to others in a network, the greater their centrality in the network.
In-degree and out-degree variables are related to centrality.
- In-degree centrality concentrates on a specific individual as the point of focus; centrality of all other individuals is based on their relation to the focal point of the "in-degree" individual.
- Out-degree is a measure of centrality that still focuses on a single individual, but the analytic is concerned with the out-going interactions of the individual; the measure of out-degree centrality is how many times the focus point individual interacts with others.
- A sociogram is a visualization with defined boundaries of connections in the network. For example, a sociogram which shows out-degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network.
Unique capabilities
Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a networked learning community and can help illustrate the extent of the participants' interactions with the other members of the group. The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.
A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence, a greater regard for the recommendations of "central" participants, infrequency of cross-gender interaction in a network, and the relatively small role played by an instructor in an asynchronous learning network.
Other methods used alongside SNA
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field, researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL. Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.
A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data triangulation, which will lead to an increase of evaluation reliability in CSCL studies.
- Qualitative method – The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences.
- Ethnographic data such as student questionnaires and interviews and classroom non-participant observations
- Case studies: comprehensively study particular CSCL situations and relate findings to general schemes
- Content analysis: offers information about the content of the communication among members
- Quantitative method – This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies.
- Computer log files: provide automatic data on how collaborative tools are used by learners
- Multidimensional scaling (MDS): charts similarities among actors, so that more similar input data is closer together
- Software tools: QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST
See also
- Actor-network theory
- Attention inequality
- Blockmodeling
- Community structure
- Complex network
- Digital humanities
- Dynamic network analysis
- Friendship paradox
- Individual mobility
- Influence-for-hire
- Mathematical sociology
- Metcalfe's law
- Netocracy
- Network-based diffusion analysis
- Network science
- Organizational patterns
- Small world phenomenon
- Social media analytics
- Social media intelligence
- Social media mining
- Social network
- Social network analysis software
- Social networking service
- Social software
- Social web
- Sociomapping
- Virtual collective consciousness
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Further reading
This "Further reading" section may need cleanup.(December 2021) |
- Introduction to Stochastic Actor-Based Models for Network Dynamics – Snijders et al.
- Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon
- NetLab at the University of Toronto, studies the intersection of social, communication, information and computing networks
- Program on Networked Governance, Harvard University
- Historical Dynamics in a time of Crisis: Late Byzantium, 1204–1453 (a discussion of social network analysis from the point of view of historical studies)
- Social Network Analysis: A Systematic Approach for Investigating
- Networks, Crowds, and Markets (2010) by D. Easley & J. Kleinberg
- Introduction to Social Networks Methods (2005) by R. Hanneman & M. Riddle
- Social Network Analysis with Applications (2013) by I. McCulloh, H. Armstrong & A. Johnson
External links
- International Network for Social Network Analysis
- Awesome Network Analysis – 200+ links to books, conferences, courses, journals, research groups, software, tutorials and more
- Netwiki – wiki page devoted to social networks; maintained at University of North Carolina at Chapel Hill
Social network analysis SNA is the process of investigating social structures through the use of networks and graph theory It characterizes networked structures in terms of nodes individual actors people or things within the network and the ties edges or links relationships or interactions that connect them Examples of social structures commonly visualized through social network analysis include social media networks meme proliferation information circulation friendship and acquaintance networks business networks knowledge networks difficult working relationships collaboration graphs kinship disease transmission and sexual relationships These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest A social network diagram displaying friendship ties among a set of Facebook users Social network analysis has emerged as a key technique in modern sociology It has also gained significant popularity in the following anthropology biology demography communication studies economics geography history information science organizational studies physics political science public health social psychology development studies sociolinguistics and computer science education and distance education research and is now commonly available as a consumer tool see the list of SNA software HistorySocial network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Emile Durkheim who wrote about the importance of studying patterns of relationships that connect social actors Social scientists have used the concept of social networks since early in the 20th century to connote complex sets of relationships between members of social systems at all scales from interpersonal to international In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods In 1954 John Arundel Barnes started using the term systematically to denote patterns of ties encompassing concepts traditionally used by the public and those used by social scientists bounded groups e g tribes families and social categories e g gender ethnicity Starting in the 1970s scholars such as Ronald Burt Kathleen Carley Mark Granovetter David Krackhardt Edward Laumann Anatol Rapoport Barry Wellman Douglas R White and Harrison White expanded the use of systematic social network analysis Beginning in the late 1990s social network analysis experienced a further resurgence with work by sociologists political scientists economists computer scientists and physicists such as Duncan J Watts Albert Laszlo Barabasi Peter Bearman Nicholas A Christakis James H Fowler Mark Newman Matthew Jackson Jon Kleinberg and others developing and applying new models and methods prompted in part by the emergence of new data available about online social networks as well as digital traces regarding face to face networks Computational SNA has been extensively used in research on study abroad second language acquisition Even in the study of literature network analysis has been applied by Anheier Gerhards and Romo Wouter De Nooy and Burgert Senekal Indeed social network analysis has found applications in various academic disciplines as well as practical contexts such as countering money laundering and terrorism citation needed MetricsSize The number of network members in a given network Connections Homophily The extent to which actors form ties with similar versus dissimilar others Similarity can be defined by gender race age occupation educational achievement status values or any other salient characteristic Homophily is also referred to as assortativity Multiplexity The number of content forms contained in a tie For example two people who are friends and also work together would have a multiplexity of 2 Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties Mutuality Reciprocity The extent to which two actors reciprocate each other s friendship or other interaction Network Closure A measure of the completeness of relational triads An individual s assumption of network closure i e that their friends are also friends is called transitivity Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure Propinquity The tendency for actors to have more ties with geographically close others Distributions Bridge An individual whose weak ties fill a structural hole providing the only link between two individuals or clusters It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure Centrality Centrality refers to a group of metrics that aim to quantify the importance or influence in a variety of senses of a particular node or group within a network Examples of common methods of measuring centrality include betweenness centrality closeness centrality eigenvector centrality alpha centrality and degree centrality Density The proportion of direct ties in a network relative to the total number possible Distance The minimum number of ties required to connect two particular actors as popularized by Stanley Milgram s small world experiment and the idea of six degrees of separation Structural holes The absence of ties between two parts of a network Finding and exploiting a structural hole can give an entrepreneur a competitive advantage This concept was developed by sociologist Ronald Burt and is sometimes referred to as an alternate conception of social capital Tie Strength Defined by the linear combination of time emotional intensity intimacy and reciprocity i e mutuality Strong ties are associated with homophily propinquity and transitivity while weak ties are associated with bridges Segmentation Groups are identified as cliques if every individual is directly tied to every other individual social circles if there is less stringency of direct contact which is imprecise or as structurally cohesive blocks if precision is wanted Clustering coefficient A measure of the likelihood that two associates of a node are associates A higher clustering coefficient indicates a greater cliquishness Cohesion The degree to which actors are connected directly to each other by cohesive bonds Structural cohesion refers to the minimum number of members who if removed from a group would disconnect the group Modelling and visualization of networksDifferent characteristics of social networks A B and C show varying centrality and density of networks panel D shows network closure i e when two actors tied to a common third actor tend to also form a direct tie between them Panel E represents two actors with different attributes e g organizational affiliation beliefs gender education who tend to form ties Panel F consists of two types of ties friendship solid line and dislike dashed line In this case two actors being friends both dislike a common third or similarly two actors that dislike a common third tend to be friends Visual representation of social networks is important to understand the network data and convey the result of the analysis Numerous methods of visualization for data produced by social network analysis have been presented Many of the analytic software have modules for network visualization The data is explored by displaying nodes and ties in various layouts and attributing colors size and other advanced properties to nodes Visual representations of networks may be a powerful method for conveying complex information Still care should be taken in interpreting node and graph properties from visual displays alone as they may misrepresent structural properties better captured through quantitative analyses Signed graphs can be used to illustrate good and bad relationships between humans A positive edge between two nodes denotes a positive relationship friendship alliance dating and a negative edge denotes a negative relationship hatred anger Signed social network graphs can be used to predict the future evolution of the graph In signed social networks there is the concept of balanced and unbalanced cycles A balanced cycle is defined as a cycle where the product of all the signs are positive According to balance theory balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group For example a group of 3 people A B and C where A and B have a positive relationship B and C have a positive relationship and yet C and A have a negative relationship is an unbalanced cycle This group is very likely to change into a balanced cycle such as one where B only has a good relationship with A and both A and B have a negative relationship with C By using the concepts of balanced and unbalanced graphs the evolution of a social network graph may be forecasted Different approaches to participatory network mapping have proven useful especially when using social network analysis as a tool for facilitating change Here participants interviewers provide network data by mapping the network with pen and paper or digitally during the data collection session An example of a pen and paper network mapping approach which also includes the collection of some actor attributes perceived influence and goals of actors is the Net map toolbox One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected Social networking potential Social Networking Potential SNP is a numeric coefficient derived through algorithms to represent both the size of an individual s social network and their ability to influence that network SNP coefficients were first defined and used by Bob Gerstley in 2002 A closely related term is Alpha User defined as a person with a high SNP SNP coefficients have two primary functions The classification of individuals based on their social networking potential and The weighting of respondents in quantitative marketing research studies By calculating the SNP of respondents and by targeting High SNP respondents the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced Variables used to calculate an individual s SNP include but are not limited to participation in Social Networking activities group memberships leadership roles recognition publication editing contributing to non electronic media publication editing contributing to electronic media websites blogs and frequency of past distribution of information within their network The acronym SNP and some of the first algorithms developed to quantify an individual s social networking potential were described in the white paper Advertising Research is Changing Gerstley 2003 See Viral Marketing The first book to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen Kasper and Melkko in 2004 The first book to discuss Alpha Users more generally in the context of social marketing intelligence was Communities Dominate Brands by Ahonen amp Moore in 2005 In 2012 Nicola Greco UCL presents at TEDx the Social Networking Potential as a parallelism to the potential energy that users generate and companies should use stating that SNP is the new asset that every company should aim to have Practical applicationsSocial network analysis is used extensively in a wide range of applications and disciplines Some common network analysis applications include data aggregation and mining network propagation modeling network modeling and sampling user attribute and behavior analysis community maintained resource support location based interaction analysis and filtering recommender systems development and link prediction and entity resolution In the private sector businesses use social network analysis to support activities such as customer interaction and analysis information system development analysis marketing and business intelligence needs see social media analytics Some public sector uses include development of leader engagement strategies analysis of individual and group engagement and media use and community based problem solving Longitudinal SNA in schools Large numbers of researchers worldwide examine the social networks of children and adolescents In questionnaires they list all classmates students in the same grade or schoolmates asking Who are your best friends Students may sometimes nominate as many peers as they wish other times the number of nominations is limited Social network researchers have investigated similarities in friendship networks The similarity between friends was established as far back as classical antiquity Resemblance is an important basis for the survival of friendships Similarity in characteristics attitudes or behaviors means that friends understand each other more quickly have common interests to talk about know better where they stand with each other and have more trust in each other As a result such relationships are more stable and valuable Moreover looking more alike makes young people more confident and strengthens them in developing their identity Similarity in behavior can result from two processes selection and influence These two processes can be distinguished using longitudinal social network analysis in the R package SIENA Simulation Investigation for Empirical Network Analyses developed by Tom Snijders and colleagues Longitudinal social network analysis became mainstream after the publication of a special issue of the Journal of Research on Adolescence in 2013 edited by Rene Veenstra and containing 15 empirical papers Security applications Social network analysis is also used in intelligence counter intelligence and law enforcement activities This technique allows the analysts to map covert organizations such as an espionage ring an organized crime family or a street gang The National Security Agency NSA uses its electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security The NSA looks up to three nodes deep during this network analysis After the initial mapping of the social network is complete analysis is performed to determine the structure of the network and determine for example the leaders within the network This allows military or law enforcement assets to launch capture or kill decapitation attacks on the high value targets in leadership positions to disrupt the functioning of the network The NSA has been performing social network analysis on call detail records CDRs also known as metadata since shortly after the September 11 attacks Textual analysis applications Large textual corpora can be turned into networks and then analyzed using social network analysis In these networks the nodes are Social Actors and the links are Actions The extraction of these networks can be automated by using parsers The resulting networks which can contain thousands of nodes are then analyzed using tools from network theory to identify the key actors the key communities or parties and general properties such as the robustness or structural stability of the overall network or the centrality of certain nodes This automates the approach introduced by Quantitative Narrative Analysis whereby subject verb object triplets are identified with pairs of actors linked by an action or pairs formed by actor object Narrative network of US Elections 2012 In other approaches textual analysis is carried out considering the network of words co occurring in a text In these networks nodes are words and links among them are weighted based on their frequency of co occurrence within a specific maximum range Internet applications Social network analysis has also been applied to understanding online behavior by individuals organizations and between websites Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community Netocracy Another concept that has emerged from this connection between social network theory and the Internet is the concept of netocracy where several authors have emerged studying the correlation between the extended use of online social networks and changes in social power dynamics Social media internet applications Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook In computer supported collaborative learning One of the most current methods of the application of SNA is to the study of computer supported collaborative learning CSCL When applied to CSCL SNA is used to help understand how learners collaborate in terms of amount frequency and length as well as the quality topic and strategies of communication Additionally SNA can focus on specific aspects of the network connection or the entire network as a whole It uses graphical representations written representations and data representations to help examine the connections within a CSCL network When applying SNA to a CSCL environment the interactions of the participants are treated as a social network The focus of the analysis is on the connections made among the participants how they interact and communicate as opposed to how each participant behaved on his or her own Key terms There are several key terms associated with social network analysis research in computer supported collaborative learning such as density centrality indegree outdegree and sociogram Density refers to the connections between participants Density is defined as the number of connections a participant has divided by the total possible connections a participant could have For example if there are 20 people participating each person could potentially connect to 19 other people A density of 100 19 19 is the greatest density in the system A density of 5 indicates there is only 1 of 19 possible connections Centrality focuses on the behavior of individual participants within a network It measures the extent to which an individual interacts with other individuals in the network The more an individual connects to others in a network the greater their centrality in the network In degree and out degree variables are related to centrality In degree centrality concentrates on a specific individual as the point of focus centrality of all other individuals is based on their relation to the focal point of the in degree individual Out degree is a measure of centrality that still focuses on a single individual but the analytic is concerned with the out going interactions of the individual the measure of out degree centrality is how many times the focus point individual interacts with others A sociogram is a visualization with defined boundaries of connections in the network For example a sociogram which shows out degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network Unique capabilities Researchers employ social network analysis in the study of computer supported collaborative learning in part due to the unique capabilities it offers This particular method allows the study of interaction patterns within a networked learning community and can help illustrate the extent of the participants interactions with the other members of the group The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time A number of research studies have applied SNA to CSCL across a variety of contexts The findings include the correlation between a network s density and the teacher s presence a greater regard for the recommendations of central participants infrequency of cross gender interaction in a network and the relatively small role played by an instructor in an asynchronous learning network Other methods used alongside SNA Although many studies have demonstrated the value of social network analysis within the computer supported collaborative learning field researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in depth analysis of CSCL Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences A number of research studies have combined other types of analysis with SNA in the study of CSCL This can be referred to as a multi method approach or data triangulation which will lead to an increase of evaluation reliability in CSCL studies Qualitative method The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences Ethnographic data such as student questionnaires and interviews and classroom non participant observations Case studies comprehensively study particular CSCL situations and relate findings to general schemes Content analysis offers information about the content of the communication among members Quantitative method This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies Computer log files provide automatic data on how collaborative tools are used by learners Multidimensional scaling MDS charts similarities among actors so that more similar input data is closer together Software tools QUEST SAMSA System for Adjacency Matrix and Sociogram based Analysis and Nud ISTSee alsoActor network theory Attention inequality Blockmodeling Community structure Complex network Digital humanities Dynamic network analysis Friendship paradox Individual mobility Influence for hire Mathematical sociology Metcalfe s law Netocracy Network based diffusion analysis Network science Organizational patterns Small world phenomenon Social media analytics Social media intelligence Social media mining Social network Social network analysis software Social networking service Social software Social web Sociomapping Virtual collective consciousnessReferencesOtte Evelien Rousseau Ronald 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doi 10 1086 230603 S2CID 143587142 de Nooy Wouter October 2003 Fields and networks correspondence analysis and social network analysis in the framework of field theory Poetics 31 5 6 305 327 doi 10 1016 s0304 422x 03 00035 4 Senekal Burgert December 1 2012 Die Afrikaanse literere sisteem n eksperimentele benadering met behulp van Sosiale netwerk analise SNA geesteswetenskappe The Afrikaans literary system an experimental approach using Social Network Analysis SNA humanities Litnet Akademies in Afrikaans 9 3 614 638 hdl 10520 EJC129817 McPherson Miller Smith Lovin Lynn Cook James M August 2001 Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27 1 415 444 doi 10 1146 annurev soc 27 1 415 S2CID 2341021 Podolny Joel M Baron James N October 1997 Resources and Relationships Social Networks and Mobility in the Workplace American Sociological Review 62 5 673 CiteSeerX 10 1 1 114 6822 doi 10 2307 2657354 JSTOR 2657354 Kilduff M Tsai W 2003 Social networks and organisations Sage Publications Kadushin C 2012 Understanding social networks Theories concepts and findings Oxford Oxford University Press ISBN 9780195379471 Flynn Francis J Reagans Ray E Guillory Lucia 2010 Do you two know each other Transitivity homophily and the need for network closure Journal of Personality and Social Psychology 99 5 855 869 doi 10 1037 a0020961 PMID 20954787 S2CID 6335920 Granovetter Mark S May 1973 The Strength of Weak Ties American Journal of Sociology 78 6 1360 1380 doi 10 1086 225469 S2CID 59578641 Hansen Derek et al 2010 Analyzing Social Media Networks with NodeXL Morgan Kaufmann p 32 ISBN 978 0 12 382229 1 Liu Bing 2011 Web Data Mining Exploring Hyperlinks Contents and Usage Data Springer p 271 ISBN 978 3 642 19459 7 Hanneman Robert A amp Riddle Mark 2011 Concepts and Measures for Basic Network Analysis The Sage Handbook of Social Network Analysis SAGE pp 364 367 ISBN 978 1 84787 395 8 Tsvetovat Maksim amp Kouznetsov Alexander 2011 Social Network Analysis for Startups Finding Connections on the Social Web O Reilly p 45 ISBN 978 1 4493 1762 1 The most comprehensive reference is Wasserman Stanley amp Faust Katherine 1994 Social Networks Analysis Methods and Applications Cambridge Cambridge University Press A short clear basic summary is in Krebs Valdis 2000 The Social Life of Routers Internet Protocol Journal 3 December 14 25 Opsahl Tore Agneessens Filip Skvoretz John July 2010 Node centrality in weighted networks Generalizing degree and shortest paths Social Networks 32 3 245 251 doi 10 1016 j socnet 2010 03 006 Social Network Analysis PDF Field Manual 3 24 Counterinsurgency Headquarters Department of the Army pp B 11 B 12 Xu Guandong et al 2010 Web Mining and Social Networking Techniques and Applications Springer p 25 ISBN 978 1 4419 7734 2 Cohesive blocking is the R program for computing structural cohesion according to the Moody White 2003 algorithm This wiki site provides numerous examples and a tutorial for use with R Hanneman Robert A amp Riddle Mark 2011 Concepts and Measures for Basic Network Analysis The Sage Handbook of Social Network Analysis SAGE pp 346 347 ISBN 978 1 84787 395 8 Moody James White Douglas R February 2003 Structural Cohesion and Embeddedness A Hierarchical Concept of Social Groups American Sociological Review 68 1 103 CiteSeerX 10 1 1 18 5695 doi 10 2307 3088904 JSTOR 3088904 S2CID 142591846 Pattillo Jeffrey et al 2011 Clique relaxation models in social network analysis In Thai My T amp Pardalos Panos M eds Handbook of Optimization in Complex Networks Communication and Social Networks Springer p 149 ISBN 978 1 4614 0856 7 Linton C Freeman Visualizing Social Networks Journal of Social Structure 1 Hamdaqa Mohammad Tahvildari Ladan LaChapelle Neil Campbell Brian 2014 Cultural Scene Detection Using Reverse Louvain Optimization Science of Computer Programming 95 44 72 doi 10 1016 j scico 2014 01 006 Bacher R 1995 Graphical interaction and visualization for the analysis and interpretation of contingency analysis results Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result Proceedings of the 1995 Power Industry Computer Applications Salt Lake City USA IEEE Power Engineering Society pp 128 134 doi 10 1109 PICA 1995 515175 ISBN 0 7803 2663 6 Caschera M C Ferri F Grifoni P 2008 SIM A dynamic multidimensional visualization method for social networks PsychNology Journal 6 3 291 320 Network Analysis and Modeling CSCI 5352 danlarremore com Retrieved December 2 2024 McGrath Cathleen Blythe Jim Krackhardt David August 1997 The effect of spatial arrangement on judgments and errors in interpreting graphs Social Networks 19 3 223 242 CiteSeerX 10 1 1 121 5856 doi 10 1016 S0378 8733 96 00299 7 Cartwright Dorwin Harary Frank 1956 Structural balance a generalization of Heider s theory Psychological Review 63 5 277 293 doi 10 1037 h0046049 PMID 13359597 S2CID 14779113 Hogan Bernie Carrasco Juan Antonio Wellman Barry May 2007 Visualizing Personal Networks Working with Participant aided Sociograms Field Methods 19 2 116 144 doi 10 1177 1525822X06298589 S2CID 61291563 Anger Isabel Kittl Christian 2011 Measuring influence on Twitter Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies i KNOW 11 p 1 doi 10 1145 2024288 2024326 ISBN 9781450307321 S2CID 30427 Riquelme Fabian Gonzalez Cantergiani Pablo September 2016 Measuring user influence on Twitter A survey Information Processing amp Management 52 5 949 975 arXiv 1508 07951 doi 10 1016 j ipm 2016 04 003 S2CID 16343144 Hrsg Sara Rosengren 2013 The Changing Roles of Advertising Wiesbaden Springer Fachmedien Wiesbaden GmbH ISBN 9783658023645 Retrieved October 22 2015 page needed Ahonen T T Kasper T amp Melkko S 2005 3G marketing communities and strategic partnerships John Wiley amp Sons technology Watch TEDxMilano Nicola Greco on math and social network Video at TEDxTalks TEDxTalks Golbeck J 2013 Analyzing the Social Web Morgan Kaufmann ISBN 978 0 12 405856 9 Aram Michael Neumann Gustaf July 1 2015 Multilayered analysis of co development of business information systems Journal of Internet Services and Applications 6 1 13 doi 10 1186 s13174 015 0030 8 S2CID 16502371 McPherson Miller Smith Lovin Lynn Cook James M 2001 Birds of a Feather Homophily in Social Networks Annual Review of Sociology 27 1 415 444 doi 10 1146 annurev soc 27 1 415 ISSN 0360 0572 S2CID 2341021 Laursen Brett Veenstra Rene 2021 Toward understanding the functions of peer influence A summary and synthesis of recent empirical research Journal of Research on Adolescence 31 4 889 907 doi 10 1111 jora 12606 ISSN 1050 8392 PMC 8630732 PMID 34820944 Hallinan M T 1980 Patterns of cliquing among youth In H C Foot A J Chapman amp J R Smith Eds Friendship and social relations in children pp 321 342 New York NY Wiley psycnet apa org Retrieved March 10 2023 Snijders Tom A B van de Bunt Gerhard G Steglich Christian E G 2010 Introduction to stochastic actor based models for network dynamics Social Networks Dynamics of Social Networks 32 1 44 60 doi 10 1016 j socnet 2009 02 004 ISSN 0378 8733 Veenstra Rene Laninga Wijnen Lydia 2023 The Prominence of Peer Interactions Relationships and Networks in Adolescence and Early Adulthood osf io American Psychological Association Retrieved March 10 2023 Ackerman Spencer July 17 2013 NSA warned to rein in surveillance as agency reveals even greater scope The Guardian Retrieved July 19 2013 How The NSA Uses Social Network Analysis To Map Terrorist Networks June 12 2013 Retrieved July 19 2013 NSA Using Social Network Analysis Wired May 12 2006 Retrieved July 19 2013 Dryer Alexander May 11 2006 NSA has massive database of Americans phone calls Slate Retrieved July 19 2013 Sudhahar Saatviga De Fazio Gianluca Franzosi Roberto Cristianini Nello January 2015 Network analysis of narrative content in large corpora Natural Language Engineering 21 1 81 112 doi 10 1017 S1351324913000247 hdl 1983 dfb87140 42e2 486a 91d5 55f9007042df S2CID 3385681 Quantitative Narrative Analysis Roberto Franzosi Emory University c 2010 Sudhahar Saatviga Veltri Giuseppe A Cristianini Nello May 2015 Automated analysis of the US presidential elections using Big Data and network analysis Big Data amp Society 2 1 doi 10 1177 2053951715572916 hdl 2381 31767 Osterbur Megan Kiel Christina April 2017 A hegemon fighting for equal rights the dominant role of COC Nederland in the LGBT transnational advocacy network Global Networks 17 2 234 254 doi 10 1111 glob 12126 Brettschneider Marla Burgess Susan Keating Christine eds September 19 2017 Pink Links Visualizing the Global LGBTQ Network LGBTQ Politics New York University Press pp 493 522 doi 10 18574 nyu 9781479849468 003 0034 ISBN 978 1 4798 4946 8 Bard Alexander Ssderqvist Jan February 24 2012 The Netocracts Futurica Trilogy 1 Stockholm Text ISBN 9789187173004 Retrieved March 3 2017 Kwak Haewoon Lee Changhyun Park Hosung Moon Sue April 26 2010 What is Twitter a social network or a news media Proceedings of the 19th international conference on World wide web ACM pp 591 600 CiteSeerX 10 1 1 212 1490 doi 10 1145 1772690 1772751 ISBN 9781605587998 S2CID 207178765 Laat Maarten de Lally Vic Lipponen Lasse Simons Robert Jan March 8 2007 Investigating patterns of interaction in networked learning and computer supported collaborative learning A role for Social Network Analysis International Journal of Computer Supported Collaborative Learning 2 1 87 103 doi 10 1007 s11412 007 9006 4 S2CID 3238474 Patterns of Interaction in Computer supported Learning A Social Network Analysis International Conference of the Learning Sciences 2013 pp 346 351 doi 10 4324 9780203763865 71 ISBN 9780203763865 Marti nez A Dimitriadis Y Rubia B Gomez E de la Fuente P December 2003 Combining qualitative evaluation and social network analysis for the study of classroom social interactions Computers amp Education 41 4 353 368 CiteSeerX 10 1 1 114 7474 doi 10 1016 j compedu 2003 06 001 S2CID 10636524 Cho H Stefanone M amp Gay G 2002 Social information sharing in a CSCL community Computer support for collaborative learning Foundations for a CSCL community Hillsdale NJ Lawrence Erlbaum pp 43 50 CiteSeerX 10 1 1 225 5273 Aviv R Erlich Z Ravid G amp Geva A 2003 Network analysis of knowledge construction in asynchronous learning networks Journal of Asynchronous Learning Networks 7 3 1 23 CiteSeerX 10 1 1 2 9044 Daradoumis Thanasis Martinez Mones Alejandra Xhafa Fatos September 5 2004 An Integrated Approach for Analysing and Assessing the Performance of Virtual Learning Groups In Vreede Gert Jan de Guerrero Luis A Raventos Gabriela Marin eds Groupware Design Implementation and Use Lecture Notes in Computer Science Vol 3198 Springer Berlin Heidelberg pp 289 304 doi 10 1007 978 3 540 30112 7 25 hdl 2117 116654 ISBN 9783540230168 S2CID 6605 Martinez A Dimitriadis Y Rubia B Gomez E de la Fuente P December 1 2003 Combining qualitative evaluation and social network analysis for the study of classroom social interactions Computers amp Education Documenting Collaborative Interactions Issues and Approaches 41 4 353 368 CiteSeerX 10 1 1 114 7474 doi 10 1016 j compedu 2003 06 001 S2CID 10636524 Johnson Karen E January 1 1996 Review of The Art of Case Study Research The Modern Language Journal 80 4 556 557 doi 10 2307 329758 JSTOR 329758 Further readingThis Further reading section may need cleanup Please read the editing guide and help improve the section December 2021 Learn how and when to remove this message Introduction to Stochastic Actor Based Models for Network Dynamics Snijders et al Center for Computational Analysis of Social and Organizational Systems CASOS at Carnegie Mellon NetLab at the University of Toronto studies the intersection of social communication information and computing networks Program on Networked Governance Harvard University Historical Dynamics in a time of Crisis Late Byzantium 1204 1453 a discussion of social network analysis from the point of view of historical studies Social Network Analysis A Systematic Approach for Investigating Networks Crowds and Markets 2010 by D Easley amp J Kleinberg Introduction to Social Networks Methods 2005 by R Hanneman amp M Riddle Social Network Analysis with Applications 2013 by I McCulloh H Armstrong amp A JohnsonExternal linksInternational Network for Social Network Analysis Awesome Network Analysis 200 links to books conferences courses journals research groups software tutorials and more Netwiki wiki page devoted to social networks maintained at University of North Carolina at Chapel HillWikimedia Commons has media related to Social network analysis