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Social network analysis

This article is about the theoretical concept. For quantitative application to social media, see social media analytics. For social networking sites, see social networking service. For other uses, see Social network (disambiguation).

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, memes spread, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, social networks, 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, political science, public health, social psychology, development studies, sociolinguistics, and computer science and is now commonly available as a consumer tool (see the list of SNA software).

The advantages of SNA are twofold. Firstly, it can process a large amount of relational data and describe the overall relational network structure. tem and parameter selection to confirm the influential nodes in the network, such as in-degree and out-degree centrality. SNA context and choose which parameters to define the “center” according to the characteristics of the network. Through analyzing nodes, clusters and relations, the communication structure and position of individuals can be clearly described

Contents

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). 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.

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 applications such as countering money laundering and terrorism.

Hue (from red=0 to blue=max) indicates each node's betweenness centrality.

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.

Different 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. Exploration of the data is done through 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, but 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 between two nodes 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, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph 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 concept of balanced and unbalanced cycles, the evolution of signed social network graphs can be predicted.

Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out 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

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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:

  1. The classification of individuals based on their social networking potential, and
  2. 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".

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, social sharing 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.

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 analysed with the method of 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 analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or 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.

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
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Social network analysis
Social network analysis Language Watch Edit 160 160 Redirected from Social Network Analysis This article is about the theoretical concept For quantitative application to social media see social media analytics For social networking sites see social networking service For other uses see Social network disambiguation Social network analysis SNA is the process of investigating social structures through the use of networks and graph theory 1 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 2 3 memes spread 4 information circulation 5 friendship and acquaintance networks business networks knowledge networks 6 7 difficult working relationships 8 social networks collaboration graphs kinship disease transmission and sexual relationships 9 10 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 11 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 12 demography communication studies 3 13 economics geography history information science organizational studies 6 8 political science 14 public health 15 7 social psychology development studies sociolinguistics and computer science 16 and is now commonly available as a consumer tool see the list of SNA software 17 18 19 20 The advantages of SNA are twofold Firstly it can process a large amount of relational data and describe the overall relational network structure tem and parameter selection to confirm the influential nodes in the network such as in degree and out degree centrality SNA context and choose which parameters to define the center according to the characteristics of the network Through analyzing nodes clusters and relations the communication structure and position of individuals can be clearly described 21 Contents 1 History 2 Metrics 2 1 Connections 2 2 Distributions 2 3 Segmentation 3 Modelling and visualization of networks 3 1 Social networking potential 4 Practical applications 4 1 Security applications 4 2 Textual analysis applications 4 3 Internet applications 4 3 1 Social media internet applications 4 4 In computer supported collaborative learning 4 4 1 Key terms 4 4 2 Unique capabilities 4 4 3 Other methods used alongside SNA 5 See also 6 References 7 External links 7 1 Further reading 7 2 Organizations 7 3 Peer reviewed journals 7 4 Textbooks and educational resourcesHistory EditSocial 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 22 In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods 22 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 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 23 SNA has been extensively used in research on study abroad second language acquisition 24 Even in the study of literature network analysis has been applied by Anheier Gerhards and Romo 25 Wouter De Nooy 26 and Burgert Senekal 27 Indeed social network analysis has found applications in various academic disciplines as well as practical applications such as countering money laundering and terrorism Metrics Edit Hue from red 0 to blue max indicates each node s betweenness centrality Size The number of network members in a given network Connections Edit 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 28 Homophily is also referred to as assortativity Multiplexity The number of content forms contained in a tie 29 For example two people who are friends and also work together would have a multiplexity of 2 30 Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties 8 Mutuality Reciprocity The extent to which two actors reciprocate each other s friendship or other interaction 31 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 32 Propinquity The tendency for actors to have more ties with geographically close others Distributions Edit 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 33 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 34 35 36 37 Examples of common methods of measuring centrality include betweenness centrality 38 closeness centrality eigenvector centrality alpha centrality and degree centrality 39 Density The proportion of direct ties in a network relative to the total number possible 40 41 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 33 Strong ties are associated with homophily propinquity and transitivity while weak ties are associated with bridges Segmentation Edit 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 42 Clustering coefficient A measure of the likelihood that two associates of a node are associates A higher clustering coefficient indicates a greater cliquishness 43 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 44 45 Modelling and visualization of networks Edit Different 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 46 Numerous methods of visualization for data produced by social network analysis have been presented 47 48 49 Many of the analytic software have modules for network visualization Exploration of the data is done through 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 but 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 50 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 between two nodes 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 but C and A have a negative relationship is an unbalanced cycle This group is very likely to morph 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 concept of balanced and unbalanced cycles the evolution of signed social network graphs can be predicted 51 Especially when using social network analysis as a tool for facilitating change different approaches of participatory network mapping have proven useful Here participants interviewers provide network data by actually mapping out 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 52 Social networking potential Edit This section may require cleanup to meet Wikipedia s quality standards The specific problem is More careful cleanup after merge required Please help improve this section if you can December 2015 Learn how and when to remove this template message Social Networking Potential SNP is a numeric coefficient derived through algorithms 53 54 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 55 The first book 56 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 57 Practical applications EditSee also Social network analysis criminology 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 social sharing and filtering recommender systems development and link prediction and entity resolution 58 In the private sector businesses use social network analysis to support activities such as customer interaction and analysis information system development analysis 59 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 Security applications Edit 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 60 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 61 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 62 63 Textual analysis applications Edit Large textual corpora can be turned into networks and then analysed with the method of 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 analysed by using tools from network theory to identify the key actors the key communities or parties and general properties such as robustness or structural stability of the overall network or centrality of certain nodes 64 This automates the approach introduced by Quantitative Narrative Analysis 65 whereby subject verb object triplets are identified with pairs of actors linked by an action or pairs formed by actor object 66 Narrative network of US Elections 2012 66 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 Edit Social network analysis has also been applied to understanding online behavior by individuals organizations and between websites 16 Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web 67 The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community 68 Social media internet applications Edit 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 69 In computer supported collaborative learning Edit 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 70 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 70 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 Edit 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 70 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 70 13 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 70 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 70 13 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 70 Unique capabilities Edit 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 70 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 71 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 70 a greater regard for the recommendations of central participants 72 infrequency of cross gender interaction in a network 73 and the relatively small role played by an instructor in an asynchronous learning network 74 Other methods used alongside SNA Edit Although many studies have demonstrated the value of social network analysis within the computer supported collaborative learning field 70 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 75 Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences 76 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 77 Ethnographic data such as student questionnaires and interviews and classroom non participant observations 76 Case studies comprehensively study particular CSCL situations and relate findings to general schemes 76 Content analysis offers information about the content of the communication among members 76 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 76 Multidimensional scaling MDS charts similarities among actors so that more similar input data is closer together 76 Software tools QUEST SAMSA System for Adjacency Matrix and Sociogram based Analysis and Nud IST 76 See also EditActor network theory Attention inequality Blockmodeling Community structure Complex network Digital humanities Dynamic network analysis Friendship paradox Individual mobility Mathematical sociology Metcalfe s law Network based diffusion analysis Network science Organizational patterns Small world phenomenon Social media analytics Social media mining Social network Social network analysis software Social networking service Social software Social web SociomappingReferences Edit Otte Evelien Rousseau Ronald 2002 Social network analysis a powerful strategy also for the information sciences Journal of Information Science 28 6 441 453 doi 10 1177 016555150202800601 S2CID 17454166 Grandjean Martin 2016 A social network analysis of Twitter Mapping the digital humanities community Cogent Arts amp Humanities 3 1 1171458 doi 10 1080 23311983 2016 1171458 a b Hagen L Neely S Robert Cooperman C Keller T DePaula N 2018 Crisis Communications in the Age of Social Media A Network Analysis of Zika Related Tweets Soc Sci Comput Rev Social Science Computer Review 36 5 523 541 doi 10 1177 0894439317721985 ISSN 0894 4393 OCLC 7323548177 S2CID 67362137 Nasrinpour Hamid Reza Friesen Marcia R McLeod Robert D 2016 11 22 An Agent Based Model of Message Propagation in the Facebook Electronic Social Network arXiv 1611 07454 cs SI Grandjean Martin 2017 Complex structures and international organizations Analisi e visualizzazioni delle reti in storia L esempio della cooperazione intellettuale della Societa delle Nazioni Memoria e Ricerca 2 371 393 doi 10 14647 87204 See also French version PDF and English summary a b Brennecke Julia Rank Olaf 2017 05 01 The firm s knowledge network and the transfer of advice among corporate inventors A multilevel network study Research Policy 46 4 768 783 doi 10 1016 j respol 2017 02 002 ISSN 0048 7333 a b Harris Jenine K Luke Douglas A Shelton Sarah C Zuckerman Rachael B 2009 Forty Years of Secondhand Smoke Research The Gap Between Discovery and Delivery American Journal of Preventive Medicine 36 6 538 548 doi 10 1016 j amepre 2009 01 039 ISSN 0749 3797 OCLC 6980180781 PMID 19372026 a b c Brennecke Julia 2019 Dissonant Ties in Intraorganizational Networks Why Individuals Seek Problem Solving Assistance from Difficult Colleagues Academy of Management Journal 63 3 743 778 doi 10 5465 amj 2017 0399 ISSN 0001 4273 OCLC 8163488129 S2CID 164852065 Pinheiro Carlos A R 2011 Social Network Analysis in Telecommunications John Wiley amp Sons p 4 ISBN 978 1 118 01094 5 D Andrea Alessia et al 2009 An Overview of Methods for Virtual Social Network Analysis In Abraham Ajith ed Computational Social Network Analysis Trends Tools and Research Advances Springer p 8 ISBN 978 1 84882 228 3 Grunspan Daniel January 23 2014 Understanding Classrooms through Social Network Analysis A Primer for Social Network Analysis in Education Research CBE Life Sciences Education 13 2 167 178 doi 10 1187 cbe 13 08 0162 PMC 4041496 PMID 26086650 Tringali Angela Sherer David L Cosgrove Jillian Bowman Reed 2020 02 10 Life history stage explains behavior in a social network before and during the early breeding season in a cooperatively breeding bird PeerJ 8 e8302 doi 10 7717 peerj 8302 ISSN 2167 8359 PMC 7020825 PMID 32095315 a b c Social network differences of chronotypes identified from mobile phone data 2018 OCLC 1062367169 Kim Rakhyun E 2020 Is Global Governance Fragmented Polycentric or Complex The State of the Art of the Network Approach International Studies Review 22 4 903 931 doi 10 1093 isr viz052 ISSN 1521 9488 Harris J K Clements B 2007 Using social network analysis to understand Missouri s system of public health emergency planners Public Health Rep Public Health Reports 122 4 488 498 doi 10 1177 003335490712200410 ISSN 0033 3549 OCLC 8062393936 PMC 1888499 PMID 17639652 a b Ghanbarnejad Fakhteh Saha Roy Rishiraj Karimi Fariba Delvenne Jean Charles Mitra Bivas 2019 Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches Cham Springer International Publishing Imprint Springer ISBN 9783030146832 OCLC 1115074203 Facebook friends mapped by Wolfram Alpha app BBC News September 24 2012 Retrieved July 25 2016 Frederic Lardinois August 30 2012 Wolfram Alpha Launches Personal Analytics Reports For Facebook Tech Crunch Retrieved July 25 2016 Institute of Reproductive Health Ivaldi M Ferreri L Daolio F Giacobini M Tomassini M Rainoldi A We Sport from academy spin off to data base for complex network analysis an innovative approach to a new technology J Sports Med and Phys Fitnes 51 suppl 1 to issue 3 The social network analysis was used to analyze properties of the network We Sport com allowing a deep interpretation and analysis of the level of aggregation phenomena in the specific context of sport and physical exercise Luo et al 2022 Economic development and construction safety research a bibliometrics approach Safety Science 145 105519 https www sciencedirect com science article abs pii S0925753521002551 a b Freeman L C 2004 The development of social network analysis a study in the sociology of science Vancouver B C Empirical Press Linton Freeman 2006 The Development of Social Network Analysis Vancouver Empirical Press Paradowski Michal B Jarynowski Andrzej Jelinska Magdalena Czopek Karolina 2021 Out of class peer interactions matter for second language acquisition during short term overseas sojourns The contributions of Social Network Analysis Selected poster presentations from the American Association of Applied Linguistics conference Denver USA March 2020 Language Teaching 54 1 139 143 doi 10 1017 S0261444820000580 S2CID 228863564 Anheier H K Gerhards J Romo F P 1995 Forms of capital and social structure of fields examining Bourdieu s social topography American Journal of Sociology 100 4 859 903 doi 10 1086 230603 S2CID 143587142 De Nooy W 2003 Fields and networks Correspondence analysis and social network analysis in the framework of Field Theory Poetics 31 5 6 305 27 doi 10 1016 s0304 422x 03 00035 4 Senekal B A 2012 Die Afrikaanse literere sisteem ʼn Eksperimentele benadering met behulp van Sosiale netwerk analise SNA LitNet Akademies 9 3 McPherson N Smith Lovin L Cook J M 2001 Birds of a feather Homophily in social networks Annual Review of Sociology 27 415 444 doi 10 1146 annurev soc 27 1 415 S2CID 2341021 Podolny J M amp Baron J N 1997 Resources and relationships Social networks and mobility in the workplace American Sociological Review 62 5 673 693 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 F J Reagans R E Guillory L 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 a b Granovetter M 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 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 amp Douglas R White 2003 Structural Cohesion and Embeddedness A Hierarchical Concept of Social Groups PDF American Sociological Review 68 1 103 127 CiteSeerX 10 1 1 18 5695 doi 10 2307 3088904 JSTOR 3088904 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 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 Caschera M C Ferri F Grifoni P 2008 SIM A dynamic multidimensional visualization method for social networks PsychNology Journal 6 3 291 320 McGrath Blythe amp Krackhardt 1997 The effect of spatial arrangement on judgements and errors in interpreting graphs PDF Social Networks 19 3 223 242 CiteSeerX 10 1 1 121 5856 doi 10 1016 S0378 8733 96 00299 7 Cartwright D Frank Harary 1956 Structural balance a generalization of Heider s theory PDF Psychological Review 63 5 277 293 doi 10 1037 h0046049 PMID 13359597 Link from Stanford University Bernie Hogan Juan Antonio Carrasco amp Barry Wellman May 2007 Visualizing Personal Networks Working with Participant Aided Sociograms PDF Field Methods 19 2 116 144 doi 10 1177 1525822X06298589 S2CID 61291563 e g Anger I amp Kittl C 2011 September Measuring influence on Twitter In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies p 31 ACM Riquelme F amp Gonzalez Cantergiani P 2016 Measuring user influence on Twitter A survey Information Processing amp Management 52 p 949 975 Hrsg Sara Rosengren 2013 The Changing Roles of Advertising Wiesbaden Springer Fachmedien Wiesbaden GmbH ISBN 9783658023645 Retrieved 22 October 2015 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 2015 07 01 Multilayered analysis of co development of business information systems PDF Journal of Internet Services and Applications 6 1 doi 10 1186 s13174 015 0030 8 S2CID 16502371 Ackerman Spencer 17 July 2013 NSA warned to rein in surveillance as agency reveals even greater scope The Guardian Retrieved 19 July 2013 How The NSA Uses Social Network Analysis To Map Terrorist Networks 12 June 2013 Retrieved 19 Jul 2013 NSA Using Social Network Analysis Wired 12 May 2006 Retrieved 19 July 2013 NSA has massive database of Americans phone calls 11 May 2006 Retrieved 19 July 2013 Sudhahar S De Fazio G Franzosi R Cristianini N 2013 Network analysis of narrative content in large corpora Natural Language Engineering 21 1 1 32 doi 10 1017 S1351324913000247 hdl 1983 dfb87140 42e2 486a 91d5 55f9007042df S2CID 3385681 Quantitative Narrative Analysis Roberto Franzosi Emory University c 2010 a b Sudhahar S Veltri GA Cristianini N 2015 Automated analysis of the US presidential elections using Big Data and network analysis Big Data amp Society 2 1 1 28 doi 10 1177 2053951715572916 OSTERBUR MEGAN KIEL CHRISTINA 2016 05 02 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 ISSN 1470 2266 Osterbur Megan E and Christina Kiel Pink Links Visualizing the Global LGBTQ Network in LGBTQ Politics A Critical Reader eds Marla Brettschneider Susan Burgess Christine Keating pg493 522 Kwak Haewoon Lee Changhyun Park Hosung Moon Sue 2010 04 26 What is Twitter a social network or a news media ACM pp 591 600 CiteSeerX 10 1 1 212 1490 doi 10 1145 1772690 1772751 ISBN 9781605587998 S2CID 207178765 a b c d e f g h i j Laat Maarten de Lally Vic Lipponen Lasse Simons Robert Jan 2007 03 08 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 Palonen T amp Hakkarainen K B Fishman amp S O Connor Divelbiss eds Patterns of Interaction in Computer Supported Learning A Social Network Analysis PDF Fourth International Conference of the Learning Sciences Mahwah NJ Erlbaum pp 334 339 Martinez A Dimitriadis Y Rubia B Gomez E de la Fuente P 2003 12 01 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 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 2004 09 05 Vreede Gert Jan de Guerrero Luis A Raventos Gabriela Marin eds Groupware Design Implementation and Use Lecture Notes in Computer Science Springer Berlin Heidelberg pp 289 304 doi 10 1007 978 3 540 30112 7 25 hdl 2117 116654 ISBN 9783540230168 a b c d e f g Martinez A Dimitriadis Y Rubia B Gomez E de la Fuente P 2003 12 01 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 Johnson Karen E 1996 01 01 Review of The Art of Case Study Research The Modern Language Journal 80 4 556 557 doi 10 2307 329758 JSTOR 329758 External links EditThis article s use of external links may not follow Wikipedia s policies or guidelines Please improve this article by removing excessive or inappropriate external links and converting useful links where appropriate into footnote references January 2017 Learn how and when to remove this template message Further reading Edit Awesome Network Analysis 200 links to books conferences courses journals research groups software tutorials and more 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 Netwiki wiki page devoted to social networks maintained at University of North Carolina at Chapel Hill Program on Networked Governance Program on Networked Governance Harvard University The International Workshop on Social Network Analysis and Mining SNA KDD An annual workshop on social network analysis and mining with participants from computer science social science and related disciplines 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 InvestigatingOrganizations Edit International Network for Social Network AnalysisPeer reviewed journals Edit Social Networks Network Science Journal of Social Structure Journal of Complex Networks Journal of Mathematical Sociology Social Network Analysis and Mining SNAM REDES Spain Universidad Autonoma de Barcelona y Universidad de Sevilla Cite journal requires journal help Connections International Network for Social Network Analysis Archived from the original on 2013 07 18 Cite journal requires journal help Textbooks and educational resources Edit 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 Johnson Social Network Analysis in Telecommunications 2011 by Carlos Andre Reis PinheiroWikimedia Commons has media related to Social network analysis Retrieved from https en wikipedia org w index php title Social network analysis amp oldid 1053688749, wikipedia, wiki, book,

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