View source for Social network analysis

{{short description|Analysis of social structures using network and graph theory}}
{{about|the theoretical concept|quantitative application to social media|social media analytics|social networking sites|social networking service|other uses|Social network (disambiguation)}}
{{Use mdy dates|date=October 2023}}
{{Sociology}}{{Network science}}
[[File:Kencf0618FacebookNetwork.jpg|thumb|A [[network diagram|social network diagram]] displaying friendship ties among a set of [[Facebook]] users]]

'''Social network analysis''' ('''SNA''') is the process of investigating social structures through the use of [[Network theory|networks]] and [[graph theory]].<ref name="Zinilli2025">{{cite book |title=Elements of Network Science: Theory, Methods and Applications in Stata, R and Python |author=Antonio Zinilli
 |publisher=Springer, Cham |series=Statistics and Computing |year=2025 |isbn=978-3-031-84712-7 |doi=10.1007/978-3-031-84712-7 |url=https://link.springer.com/book/10.1007/978-3-031-84712-7}}</ref><ref>{{cite journal |last1=Otte |first1=Evelien |last2=Rousseau |first2=Ronald |title=Social network analysis: a powerful strategy, also for the information sciences |journal=Journal of Information Science |date=December 2002 |volume=28 |issue=6 |pages=441–453 |doi=10.1177/016555150202800601 |s2cid=17454166 }}</ref> 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 structure]]s commonly visualized through social network analysis include [[Social media|social media networks]],<ref>{{cite journal |last1=Grandjean |first1=Martin |title=A social network analysis of Twitter: Mapping the digital humanities community |journal=Cogent Arts & Humanities |date=31 December 2016 |volume=3 |issue=1 |doi=10.1080/23311983.2016.1171458 |s2cid=114999767 |doi-access=free }}</ref><ref name="Hagen L 2018 523–541">{{cite journal |last1=Hagen |first1=Loni |last2=Keller |first2=Thomas |last3=Neely |first3=Stephen |last4=DePaula |first4=Nic |last5=Robert-Cooperman |first5=Claudia |title=Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets |journal=Social Science Computer Review |date=October 2018 |volume=36 |issue=5 |pages=523–541 |doi=10.1177/0894439317721985 |oclc=7323548177 |s2cid=67362137 }}</ref> [[meme]] proliferation,<ref>{{Cite arXiv|last1=Nasrinpour|first1=Hamid Reza|last2=Friesen|first2=Marcia R.|author2-link=Marcia Friesen|last3=McLeod|first3=Robert D.|date=2016-11-22|title=An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network|eprint=1611.07454|class=cs.SI}}</ref> information circulation,<ref>{{cite journal|last=Grandjean|first=Martin|title=The Paris/Geneva Divide. A Network Analysis of the Archives of the International Committee on Intellectual Cooperation of the League of Nations|language=en|journal=Culture as Soft Power: Bridging Cultural Relations, Intellectual Cooperation, and Cultural Diplomacy|date=2022|pages=65–98|doi=10.1515/9783110744552-004|url=https://shs.hal.science/halshs-03760539/file/Grandjean_2022_TheParisGenevaDivide.pdf}}</ref> [[Weak ties|friendship and acquaintance networks]], business networks, knowledge networks,<ref name="Zinilli2025">{{cite book |title=Elements of Network Science: Theory, Methods and Applications in Stata, R and Python |author=Antonio Zinilli|publisher=Springer, Cham |series=Statistics and Computing |year=2025 |isbn=978-3-031-84712-7 |doi=10.1007/978-3-031-84712-7 |url=https://link.springer.com/book/10.1007/978-3-031-84712-7}}</ref><ref name=Brennecke2017>{{cite journal |last1=Brennecke |first1=Julia |last2=Rank |first2=Olaf |title=The firm's knowledge network and the transfer of advice among corporate inventors—A multilevel network study |journal=Research Policy |date=May 2017 |volume=46 |issue=4 |pages=768–783 |doi=10.1016/j.respol.2017.02.002 }}</ref><ref name=Harris2009>{{cite journal |last1=Harris |first1=Jenine K. |last2=Luke |first2=Douglas A. |last3=Zuckerman |first3=Rachael B. |last4=Shelton |first4=Sarah C. |title=Forty Years of Secondhand Smoke Research |journal=American Journal of Preventive Medicine |date=June 2009 |volume=36 |issue=6 |pages=538–548 |doi=10.1016/j.amepre.2009.01.039 |pmid=19372026 |oclc=6980180781 }}</ref> difficult working relationships,<ref name=Brennecke2019/> [[collaboration graph]]s, [[kinship]], [[disease transmission]], and [[sexual network|sexual relationships]].<ref>{{cite book|author=Pinheiro, Carlos A.R.|title=Social Network Analysis in Telecommunications|publisher=John Wiley & Sons|year=2011|isbn=978-1-118-01094-5|page=4|url=https://books.google.com/books?id=jP8zfL6yNGkC&pg=PA4}}</ref><ref>{{cite book|author=D'Andrea, Alessia|chapter=An Overview of Methods for Virtual Social Network Analysis|editor1=Abraham, Ajith|title=Computational Social Network Analysis: Trends, Tools and Research Advances|publisher=Springer|year=2009|isbn=978-1-84882-228-3|page=8|chapter-url=https://books.google.com/books?id=-S1KiURSfRAC&pg=PA8|display-authors=etal}}</ref> These networks are often visualized through ''[[sociogram]]s'' 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.<ref>{{Cite journal|last=Grunspan|first=Daniel|date=January 23, 2014|title=Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research|journal=CBE: Life Sciences Education|volume=13|issue=2|pages=167–178|doi=10.1187/cbe.13-08-0162|pmid=26086650|pmc=4041496}}</ref>

Social network analysis has emerged as a key technique in modern [[sociology]]. It has also gained significant popularity in the following: [[anthropology]], [[biology]],<ref>{{cite journal |last1=Tringali |first1=Angela |last2=Sherer |first2=David L. |last3=Cosgrove |first3=Jillian |last4=Bowman |first4=Reed |title=Life history stage explains behavior in a social network before and during the early breeding season in a cooperatively breeding bird |journal=PeerJ |date=10 February 2020 |volume=8 |article-number=e8302 |doi=10.7717/peerj.8302 |pmid=32095315 |pmc=7020825 |doi-access=free }}</ref> [[demography]], [[communication studies]],<ref name="Hagen L 2018 523–541"/><ref name=":1">{{Cite book|title=Social network differences of chronotypes identified from mobile phone data|date=2018 |oclc=1062367169}}{{page needed|date=November 2021}}</ref> [[economics]], [[geography]], [[history]], [[information science]], [[organizational studies]],<ref name=Brennecke2017/><ref name=Brennecke2019/> [[physics]],<ref name=Gao2023>{{cite journal |last1=Gao |first1=Min |last2=Li |first2=Zheng |last3=Li |first3=Ruichen |last4=Cui |first4=Chenhao |last5=Chen |first5=Xinyuan |last6=Ye |first6=Bodian |last7=Li |first7=Yupeng |last8=Gu |first8=Weiwei |last9=Gong |first9=Qingyuan |last10=Wang |first10=Xin |last11=Chen |first11=Yang |url=https://www.cell.com/patterns/pdf/S2666-3899(23)00218-0.pdf |title=EasyGraph: A multifunctional, cross-platform, and effective library for interdisciplinary network analysis |journal=Patterns |date=October 2023 |volume=4 |issue=10 |article-number=100839|doi=10.1016/j.patter.2023.100839  |doi-access=free |pmid=37876903|pmc=10591136 }}</ref> [[political science]],<ref>{{cite journal |last1=Kim |first1=Rakhyun E |title=Is Global Governance Fragmented, Polycentric, or Complex? The State of the Art of the Network Approach |journal=International Studies Review |date=26 November 2020 |volume=22 |issue=4 |pages=903–931 |doi=10.1093/isr/viz052 |doi-access=free }}</ref> public health,<ref name=Harris2007>{{cite journal |last1=Harris |first1=Jenine K. |last2=Clements |first2=Bruce |title=Using Social Network Analysis to Understand Missouri's System of Public Health Emergency Planners |journal=Public Health Reports |date=July 2007 |volume=122 |issue=4 |pages=488–498 |doi=10.1177/003335490712200410 |pmid=17639652 |pmc=1888499 |oclc=8062393936 }}</ref><ref name=Harris2009/> [[social psychology]], [[development studies]], [[sociolinguistics]], and [[computer science]],<ref name=Ghanbarnejad>{{Cite book|title=Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches|last1=Ghanbarnejad|first1=Fakhteh|last2=Saha Roy|first2=Rishiraj|last3=Karimi|first3=Fariba|last4=Delvenne|first4=Jean-Charles|last5=Mitra|first5=Bivas|date=2019|publisher=Springer International Publishing : Imprint: Springer|isbn=978-3-030-14683-2|location=Cham |oclc=1115074203}}{{page needed|date=November 2021}}</ref> education and distance education research,<ref>{{Cite journal |last1=Bozkurt |first1=Aras |last2=Akgun-Ozbek |first2=Ela |last3=Yilmazel |first3=Sibel |last4=Erdogdu |first4=Erdem |last5=Ucar |first5=Hasan |last6=Guler |first6=Emel |last7=Sezgin |first7=Sezan |last8=Karadeniz |first8=Abdulkadir |last9=Sen-Ersoy |first9=Nazife |last10=Goksel-Canbek |first10=Nil |last11=Dincer |first11=Gokhan Deniz |last12=Ari |first12=Suleyman |last13=Aydin |first13=Cengiz Hakan |date=2015-01-20 |title=Trends in distance education research: A content analysis of journals 2009–2013 |url=https://www.irrodl.org/index.php/irrodl/article/view/1953 |journal=The International Review of Research in Open and Distributed Learning |language=en |volume=16 |issue=1 |doi=10.19173/irrodl.v16i1.1953 |issn=1492-3831 |doi-access=free }}</ref> and is now commonly available as a consumer tool (see the [[Social network analysis software|list of SNA software]]).<ref>{{cite web|url=https://www.bbc.co.uk/news/technology-19699776|title=Facebook friends mapped by Wolfram Alpha app|work=BBC News|date=September 24, 2012|access-date=July 25, 2016}}</ref><ref>{{cite web|url=https://techcrunch.com/2012/08/30/wolfram-alpha-launches-personal-analytics-reports-for-facebook/|title=Wolfram Alpha Launches Personal Analytics Reports For Facebook|work=Tech Crunch|date= August 30, 2012|author=Frederic Lardinois|access-date=July 25, 2016}}</ref><ref>{{cite journal|author1=Ivaldi M.|author2=Ferreri L.|author3=Daolio F.|author4=Giacobini M.|author5=Tomassini M.|author6=Rainoldi A.|title=We-Sport: from academy spin-off to data-base for complex network analysis; an innovative approach to a new technology|journal=J Sports Med Phys Fitness |date=2011 |volume=51|issue=suppl. 1 to issue 3|hdl=2318/90491|quote=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.}}</ref>

==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.<ref name="Freeman"/>

In the 1930s [[Jacob Moreno]] and [[Helen Hall Jennings|Helen Jennings]] introduced basic analytical methods.<ref name="Freeman">{{cite book |last1=Freeman |first1=Linton C |title=The development of social network analysis: a study in the sociology of science |date=2004 |publisher=Empirical Press; BookSurge |isbn=978-1-59457-714-7 |oclc=429594334 }}{{page needed|date=November 2021}}</ref> 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 [[Group (sociology)|groups]] (e.g., tribes, families) and social [[Categorization|categories]] (e.g., gender, ethnicity).

Starting in the 1970s, scholars such as [[Ronald Burt]], [[Kathleen Carley]], [[Mark Granovetter]], [[David Krackhardt]], [[Edward Laumann]], [[Anatol Rapoport#Social network analysis|Anatol Rapoport]], [[Barry Wellman]], [[Douglas R. White]], and [[Harrison White]] expanded the use of systematic social network analysis.<ref name="development"/>

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 O. Jackson|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.<ref name = Paradowskietal2022>{{cite journal |last1= Paradowski |first1=Michał B. |last2=Cierpich-Kozieł |first2=Agnieszka |last3=Chen |first3=Chih-Chun |last4=Ochab |first4=Jeremi K. |title=How output outweighs input and interlocutors matter for study-abroad SLA: Computational Social Network Analysis of learner interactions |journal=The Modern Language Journal |date=2022 |volume=106 |issue=4 |pages=694–725 |doi=10.1111/modl.12811|s2cid=255247273 }}</ref><ref name = Paradowskietal2024>{{cite journal |last1= Paradowski |first1=Michał B. |last2=Whitby |first2=Nicole |last3=Czuba |first3=Michał |last4=Bródka |first4=Piotr |title=Peer interaction dynamics and L2 learning trajectories during study abroad: A longitudinal investigation using dynamic computational Social Network Analysis |journal=Language Learning |date=2024 |volume=74 |issue=S2 |pages=58–115 |doi=10.1111/lang.12681|doi-access=free }}</ref> Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal |last1=Anheier |first1=Helmut K. |last2=Gerhards |first2=Jurgen |last3=Romo |first3=Frank P. |title=Forms of Capital and Social Structure in Cultural Fields: Examining Bourdieu's Social Topography |journal=American Journal of Sociology |date=January 1995 |volume=100 |issue=4 |pages=859–903 |doi=10.1086/230603 |s2cid=143587142 }}</ref> Wouter De Nooy,<ref>{{cite journal |last1=de Nooy |first1=Wouter |title=Fields and networks: correspondence analysis and social network analysis in the framework of field theory |journal=Poetics |date=October 2003 |volume=31 |issue=5–6 |pages=305–327 |doi=10.1016/s0304-422x(03)00035-4 }}</ref> and Burgert Senekal.<ref>{{cite journal |last1=Senekal |first1=Burgert |title=Die Afrikaanse literêre sisteem : 'n eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA): geesteswetenskappe |trans-title=The Afrikaans literary system: an experimental approach using Social Network Analysis (SNA): humanities |language=Afrikaans |journal=Litnet Akademies |date=1 December 2012 |volume=9 |issue=3 |pages=614–638 |hdl=10520/EJC129817 }}</ref> 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|date=July 2024}}

==Metrics==
[[Measurement|Metrics]] encountered in social network analysis include those measuring the size, the nature of connections between members being analyzed, distribution, and segmentation - including subsets such as multiplexity, propinquity, density, centrality, and cohesion.<ref name="Zinilli2025"/>

===Size===
Size: The number of nodes 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.<ref>{{cite journal |last1=McPherson |first1=Miller |last2=Smith-Lovin |first2=Lynn |last3=Cook |first3=James M |title=Birds of a Feather: Homophily in Social Networks |journal=Annual Review of Sociology |date=August 2001 |volume=27 |issue=1 |pages=415–444 |doi=10.1146/annurev.soc.27.1.415 |s2cid=2341021 }}</ref> Homophily is also referred to as [[assortativity]].

Multiplexity: The number of content-forms contained in a tie.<ref name = "Podo97"/> For example, two people who are friends and also work together would have a multiplexity of 2.<ref>{{cite book |author1=Kilduff, M.|author2=Tsai, W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref> Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.<ref name=Brennecke2019>{{cite journal |last1=Brennecke |first1=Julia |title=Dissonant Ties in Intraorganizational Networks: Why Individuals Seek Problem-Solving Assistance from Difficult Colleagues |journal=Academy of Management Journal |date=June 2020 |volume=63 |issue=3 |pages=743–778 |doi=10.5465/amj.2017.0399 |s2cid=164852065 |oclc=8163488129 }}</ref>

Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.<ref name="Kadu12"/>

[[Triadic closure|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 [[Closure (psychology)|Need for Cognitive Closure]].<ref name="Flyn10"/>

[[Propinquity]]: The tendency for actors to have more ties with geographically close others.

===Distributions===
[[Bridge (graph theory)|Bridge]]: An individual whose weak ties fill a [[Structural holes|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.<ref name="Granovetter, M. 1973 1360–1380">{{cite journal |last1=Granovetter |first1=Mark S. |title=The Strength of Weak Ties |journal=American Journal of Sociology |date=May 1973 |volume=78 |issue=6 |pages=1360–1380 |doi=10.1086/225469 |s2cid=59578641 }}</ref>

[[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.<ref>{{cite book|author=Hansen, Derek|title=Analyzing Social Media Networks with NodeXL|publisher=Morgan Kaufmann|year=2010|isbn=978-0-12-382229-1|page=32|url=https://books.google.com/books?id=rbxPm93PRY8C&pg=PA32|display-authors=etal}}</ref><ref>{{cite book|author=Liu, Bing|title=Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data|publisher=Springer|year=2011|isbn=978-3-642-19459-7|page=271|url=https://books.google.com/books?id=jnCi0Cq1YVkC&pg=PA271}}</ref><ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|name-list-style=amp|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=364–367|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA364}}</ref><ref>{{cite book|author1=Tsvetovat, Maksim |author2=Kouznetsov, Alexander|name-list-style=amp|title=Social Network Analysis for Startups: Finding Connections on the Social Web|publisher=O'Reilly|year=2011|isbn=978-1-4493-1762-1|page=45|url=https://books.google.com/books?id=hVOxjkoLSiEC&pg=PA45}}</ref> Examples of common methods of measuring "centrality" include [[betweenness centrality]],<ref name="comprehensive"/> [[closeness centrality]], [[eigenvector centrality]], [[alpha centrality]], and [[degree centrality]].<ref>{{cite journal |last1=Opsahl |first1=Tore |last2=Agneessens |first2=Filip |last3=Skvoretz |first3=John |title=Node centrality in weighted networks: Generalizing degree and shortest paths |journal=Social Networks |date=July 2010 |volume=32 |issue=3 |pages=245–251 |doi=10.1016/j.socnet.2010.03.006 }}</ref>

[[Dense graph|Density]]: The proportion of direct ties in a network relative to the total number possible.<ref>{{cite book|chapter=Social Network Analysis|title=Field Manual 3-24: Counterinsurgency|publisher=Headquarters, [[Department of the Army]]|pages=B–11 – B–12|chapter-url=https://fas.org/irp/doddir/army/fm3-24.pdf}}</ref><ref>{{cite book|author=Xu, Guandong |title=Web Mining and Social Networking: Techniques and Applications|publisher=Springer|year=2010|isbn=978-1-4419-7734-2|page=25|url=https://books.google.com/books?id=mXo9zKeYa6cC&pg=PA25|display-authors=etal}}</ref>

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 Stuart Burt|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).<ref name="Granovetter, M. 1973 1360–1380"/> Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.

===Segmentation===
Groups are identified as '[[clique]]s' if every individual is directly tied to every other individual, '[[social circle]]s' if there is less stringency of direct contact, which is imprecise, or as [[Structural cohesion|structurally cohesive]] blocks if precision is wanted.<ref name="uci"/>

[[Clustering coefficient]]: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.<ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|name-list-style=amp|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=346–347|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA346}}</ref>

Cohesion: The degree to which actors are connected directly to each other by [[Social cohesion|cohesive bonds]]. [[Structural cohesion]] refers to the minimum number of members who, if removed from a group, would disconnect the group.<ref name="asanet"/><ref>{{cite book|author=Pattillo, Jeffrey|chapter=Clique relaxation models in social network analysis|editor1=Thai, My T.|editor2=Pardalos, Panos M.|name-list-style=amp|title=Handbook of Optimization in Complex Networks: Communication and Social Networks|publisher=Springer|year=2011|isbn=978-1-4614-0856-7|page=149|chapter-url=https://books.google.com/books?id=bdRdcHxQQLQC&pg=PA149|display-authors=etal}}</ref>

==Modelling and visualization of networks==
[[File:Social network characteristics diagram.jpg|thumb|upright=1.5|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.<ref>{{cite journal|url=http://www.cmu.edu/joss/content/articles/volume1/Freeman.html|author=Linton C. Freeman|title=Visualizing Social Networks|journal=Journal of Social Structure|volume=1}}</ref> Numerous methods of visualization for data produced by social network analysis have been presented.<ref>{{cite journal|last=Hamdaqa|first=Mohammad |author2=Tahvildari, Ladan |author2-link=Ladan Tahvildari|author3=LaChapelle, Neil |author4=Campbell, Brian|title=Cultural Scene Detection Using Reverse Louvain Optimization|journal=Science of Computer Programming|date=2014|doi=10.1016/j.scico.2014.01.006|volume=95|pages=44–72|url=https://zenodo.org/record/889712 |doi-access=free}}</ref><ref>{{cite conference|author=Bacher, R.|year=1995|title=Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result|chapter=Graphical interaction and visualization for the analysis and interpretation of contingency analysis results |conference=Proceedings of the 1995 Power Industry Computer Applications|pages=128–134|location=Salt Lake City, USA|publisher=IEEE Power Engineering Society|doi=10.1109/PICA.1995.515175|isbn=0-7803-2663-6 }}</ref><ref>{{cite journal | last1 = Caschera | first1 = M. C. | last2 = Ferri | first2 = F. | last3 = Grifoni | first3 = P. | year = 2008 | title = SIM: A dynamic multidimensional visualization method for social networks | journal = PsychNology Journal | volume = 6 | issue = 3| pages = 291–320 }}</ref><ref>{{Cite web |title=Network Analysis and Modeling (CSCI 5352) |url=https://danlarremore.com/5352/ |access-date=2024-12-02 |website=danlarremore.com}}</ref> Many of the [[Social network analysis software|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.<ref name="interpreting"/>

[[Signed graph]]s 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 network|signed social networks]], there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a [[Cycle (graph theory)|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.<ref>{{cite journal |last1=Cartwright |first1=Dorwin |last2=Harary |first2=Frank |title=Structural balance: a generalization of Heider's theory. |journal=Psychological Review |date=1956 |volume=63 |issue=5 |pages=277–293 |doi=10.1037/h0046049 |pmid=13359597 |s2cid=14779113 }}</ref>

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.<ref name="visualizing"/>

===Social networking potential===
Social Networking Potential (SNP) is a numeric [[coefficient]], derived through [[algorithm]]s,<ref>{{cite book |doi=10.1145/2024288.2024326 |chapter=Measuring influence on Twitter |title=Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW '11 |year=2011 |last1=Anger |first1=Isabel |last2=Kittl |first2=Christian |page=1 |isbn=978-1-4503-0732-1 |s2cid=30427 }}</ref><ref>{{cite journal |last1=Riquelme |first1=Fabián |last2=González-Cantergiani |first2=Pablo |title=Measuring user influence on Twitter: A survey |journal=Information Processing & Management |date=September 2016 |volume=52 |issue=5 |pages=949–975 |doi=10.1016/j.ipm.2016.04.003 |arxiv=1508.07951 |s2cid=16343144 }}</ref> 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{{who|date=June 2025}} in 2002. A closely related term is [[Social marketing intelligence#Alpha users|Alpha User]], defined as a person with a high SNP.

SNP coefficients have two primary functions:
# The [[categorization|classification]] of individuals based on their social networking potential, and
# The weighting of [[wikt:respondent|respondents]] in quantitative [[marketing research]] studies.

By calculating the SNP of respondents and by [[Behavioral targeting|targeting]] High SNP respondents, the [[Persuasion|strength]] and [[relevance]] of quantitative marketing research used to drive [[viral marketing]] strategies is enhanced.

[[Variable (research)|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]].<ref>{{cite book|last1=(Hrsg.)|first1=Sara Rosengren|title=The Changing Roles of Advertising|date=2013|publisher=Springer Fachmedien Wiesbaden GmbH|location=Wiesbaden|isbn=978-3-658-02364-5|url=https://www.springer.com/us/book/9783658023645|access-date=22 October 2015}}{{page needed|date=November 2021}}</ref>

The first book<ref>Ahonen, T. T., Kasper, T., & Melkko, S. (2005). 3G marketing: communities and strategic partnerships. John Wiley & Sons.</ref> 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 ([[University College London|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".<ref>{{cite web|url=https://tedxtalks.ted.com/video/TEDxMilano-Nicola-Greco-on-math;search%3Atag%3A"technology"|title=Watch "TEDxMilano – Nicola Greco – on math and social network" Video at TEDxTalks|work=TEDxTalks}}</ref>

==Practical applications==
{{see also|Social network analysis in criminology}}
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and [[data mining|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 system]]s development, and [[link prediction]] and entity resolution.<ref name="Golbeck" /> In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, [[information system]] development analysis,<ref>{{cite journal |last1=Aram |first1=Michael |last2=Neumann |first2=Gustaf |title=Multilayered analysis of co-development of business information systems |journal=Journal of Internet Services and Applications |date=1 July 2015 |volume=6 |issue=1 |page=13 |doi=10.1186/s13174-015-0030-8 |s2cid=16502371 |doi-access=free }}</ref> 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 [[Collaborative problem-solving group|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.<ref>{{Cite journal |last1=McPherson |first1=Miller |last2=Smith-Lovin |first2=Lynn |last3=Cook |first3=James M |date=2001 |title=Birds of a Feather: Homophily in Social Networks |url=https://www.annualreviews.org/doi/10.1146/annurev.soc.27.1.415 |journal=Annual Review of Sociology |language=en |volume=27 |issue=1 |pages=415–444 |doi=10.1146/annurev.soc.27.1.415 |s2cid=2341021 |issn=0360-0572|url-access=subscription }}</ref> 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.<ref>{{Cite journal |last1=Laursen |first1=Brett |last2=Veenstra |first2=René |date=2021 |title=Toward understanding the functions of peer influence: A summary and synthesis of recent empirical research |journal=Journal of Research on Adolescence |language=en |volume=31 |issue=4 |pages=889–907 |doi=10.1111/jora.12606 |issn=1050-8392 |pmc=8630732 |pmid=34820944}}</ref> 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.<ref>{{Cite book |last1=T. |first1=Hallinan |chapter= Patterns of cliquing among youth|chapter-url=https://psycnet.apa.org/record/1995-97220-012 |editor=H. C. Foot |editor2=A. J. Chapman |editor3=J. R. Smith |title=Friendship and social relations in children |pages= 321–342 |publisher=Transaction Publishers |via=APA Psycnet |date=1980 |language=en }}</ref> 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.<ref>{{Cite journal |last1=Snijders |first1=Tom A. B. |last2=van de Bunt |first2=Gerhard G. |last3=Steglich |first3=Christian E. G. |date=2010 |title=Introduction to stochastic actor-based models for network dynamics |url=https://www.sciencedirect.com/science/article/pii/S0378873309000069 |journal=Social Networks |series=Dynamics of Social Networks |language=en |volume=32 |issue=1 |pages=44–60 |doi=10.1016/j.socnet.2009.02.004 |issn=0378-8733|url-access=subscription }}</ref> 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.<ref>{{Cite journal |last1=Veenstra |first1=René |last2=Laninga-Wijnen |first2=Lydia |year=2023 |title=The Prominence of Peer Interactions, Relationships, and Networks in Adolescence and Early Adulthood |url=https://osf.io/preprints/socarxiv/s57zm/ |access-date=2023-03-10 |website=osf.io |publisher=American Psychological Association}}</ref>

=== 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 [[computer surveillance|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.<ref name="nsa_degrees">{{cite news|url=https://www.theguardian.com/world/2013/jul/17/nsa-surveillance-house-hearing|date=17 July 2013|access-date=19 July 2013|title=NSA warned to rein in surveillance as agency reveals even greater scope|newspaper=The Guardian|last1=Ackerman|first1=Spencer}}</ref> 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.<ref name="nsa_how">{{cite web|url=http://www.digitaltonto.com/2013/how-the-nsa-uses-social-network-analysis-to-map-terrorist-networks/|date=12 June 2013|access-date=19 Jul 2013|title=How The NSA Uses Social Network Analysis To Map Terrorist Networks}}</ref> This allows military or law enforcement assets to launch capture-or-kill [[decapitation attack]]s 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 record]]s (CDRs), also known as [[metadata]], since shortly after the [[September 11 attacks]].<ref name="NSA_SNA">{{cite magazine|url=https://www.wired.com/science/discoveries/news/2006/05/70888|title=NSA Using Social Network Analysis|magazine=Wired|date=12 May 2006|access-date=19 July 2013}}</ref><ref name="nsa_usa">{{cite journal|url=http://www.slate.com/articles/news_and_politics/explainer/2006/05/how_the_nsa_does_social_network_analysis.html|date=11 May 2006|access-date=19 July 2013|title=NSA has massive database of Americans' phone calls |journal=Slate |last1=Dryer |first1=Alexander }}</ref>

=== 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.<ref>{{cite journal |last1=Sudhahar |first1=Saatviga |last2=De Fazio |first2=Gianluca |last3=Franzosi |first3=Roberto |last4=Cristianini |first4=Nello |title=Network analysis of narrative content in large corpora |journal=Natural Language Engineering |date=January 2015 |volume=21 |issue=1 |pages=81–112 |doi=10.1017/S1351324913000247 |hdl=1983/dfb87140-42e2-486a-91d5-55f9007042df |s2cid=3385681 |url=https://research-information.bris.ac.uk/en/publications/dfb87140-42e2-486a-91d5-55f9007042df |hdl-access=free }}</ref> This automates the approach introduced by Quantitative Narrative Analysis,<ref>Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA" />
[[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal |last1=Sudhahar |first1=Saatviga |last2=Veltri |first2=Giuseppe A |last3=Cristianini |first3=Nello |title=Automated analysis of the US presidential elections using Big Data and network analysis |journal=Big Data & Society |date=May 2015 |volume=2 |issue=1 |article-number=2053951715572916 |doi=10.1177/2053951715572916 |doi-access=free |hdl=2381/31767 |hdl-access=free }}</ref>]]
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.<ref name=Ghanbarnejad/> [[Hyperlink]] analysis can be used to analyze the connections between [[website]]s or [[Web page|webpages]] to examine how information flows as individuals navigate the web.<ref>{{cite journal |last1=Osterbur |first1=Megan |last2=Kiel |first2=Christina |title=A hegemon fighting for equal rights: the dominant role of COC Nederland in the LGBT transnational advocacy network |journal=Global Networks |date=April 2017 |volume=17 |issue=2 |pages=234–254 |doi=10.1111/glob.12126 }}</ref> The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.<ref>{{cite book |chapter-url=https://www.degruyter.com/document/doi/10.18574/nyu/9781479849468.003.0034/html |chapter-url-access=subscription |doi=10.18574/nyu/9781479849468.003.0034 |isbn=978-1-4798-4946-8 |chapter=Pink Links: Visualizing the Global LGBTQ Network |date=19 September 2017 |publisher=New York University Press |editor1-first=Marla |editor1-last=Brettschneider |editor2-first=Susan |editor2-last=Burgess |editor3-first=Christine |editor3-last=Keating |title=LGBTQ Politics |pages=493–522 }}</ref>

==== Netocracy ====
Another concept that has emerged from this connection between social network theory and the Internet is the concept of [[netocracy]], the correlation between the extended use of online social networks and changes in social power dynamics.<ref>{{cite book|last1=Bard|first1=Alexander|last2=Sšderqvist|first2=Jan|title=The Netocracts: Futurica Trilogy 1|date=24 February 2012 |publisher=Stockholm Text|isbn=978-91-87173-00-4|url=https://books.google.com/books?id=TeWCBwAAQBAJ&pg=PT131|access-date=3 March 2017|language=en}}</ref>

==== 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]].<ref>{{Cite book|last1=Kwak|first1=Haewoon|last2=Lee|first2=Changhyun|last3=Park|first3=Hosung|last4=Moon|first4=Sue|title=Proceedings of the 19th international conference on World wide web |chapter=What is Twitter, a social network or a news media? |date=2010-04-26|publisher=ACM|pages=591–600|doi=10.1145/1772690.1772751|isbn=978-1-60558-799-8|citeseerx=10.1.1.212.1490|s2cid=207178765}}</ref>

===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.<ref name=":0">{{Cite journal|last1=Laat|first1=Maarten de|last2=Lally|first2=Vic|last3=Lipponen|first3=Lasse|last4=Simons|first4=Robert-Jan|date=2007-03-08|title=Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis|journal=International Journal of Computer-Supported Collaborative Learning|language=en|volume=2|issue=1|pages=87–103|doi=10.1007/s11412-007-9006-4|s2cid=3238474}}</ref> 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.<ref name=":0" /> 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.<ref name=":0" />
* '''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.<ref name=":0" /><ref name=":1" />

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.<ref name=":0" />
* '''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.<ref name=":0" /><ref name=":1" />
* 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.<ref name=":0" />

====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|networked learning community]] and can help illustrate the extent of the participants' interactions with the other members of the group.<ref name=":0" /> 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.<ref>{{cite book |doi=10.4324/9780203763865-71 |chapter=Patterns of Interaction in Computer-supported Learning: A Social Network Analysis |title=International Conference of the Learning Sciences |year=2013 |pages=346–351 |isbn=978-0-203-76386-5 }}</ref>

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,<ref name=":0" /> a greater regard for the recommendations of "central" participants,<ref>{{cite journal |last1=Martı́nez |first1=A. |last2=Dimitriadis |first2=Y. |last3=Rubia |first3=B. |last4=Gómez |first4=E. |last5=de la Fuente |first5=P. |title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions |journal=Computers & Education |date=December 2003 |volume=41 |issue=4 |pages=353–368 |doi=10.1016/j.compedu.2003.06.001 |citeseerx=10.1.1.114.7474 |s2cid=10636524 }}</ref> infrequency of cross-gender interaction in a network,<ref>{{cite conference|author1=Cho, H.|author2=Stefanone, M.|author3=Gay, G|name-list-style=amp|year=2002|title=Social information sharing in a CSCL community|conference=Computer support for collaborative learning: Foundations for a CSCL community|location=Hillsdale, NJ|publisher=Lawrence Erlbaum|pages=43–50|citeseerx=10.1.1.225.5273}}</ref> and the relatively small role played by an instructor in an [[asynchronous learning]] network.<ref>{{cite journal|author1=Aviv, R.|author2=Erlich, Z.|author3=Ravid, G.|author4=Geva, A.|name-list-style=amp|year=2003|title=Network analysis of knowledge construction in asynchronous learning networks|journal=Journal of Asynchronous Learning Networks|volume=7|issue=3|pages=1–23|citeseerx=10.1.1.2.9044}}</ref>

====Other methods used alongside SNA====
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,<ref name=":0" /> 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.<ref>{{Cite book|title=Groupware: Design, Implementation, and Use|last1=Daradoumis|first1=Thanasis|last2=Martínez-Monés|first2=Alejandra|last3=Xhafa|first3=Fatos|chapter=An Integrated Approach for Analysing and Assessing the Performance of Virtual Learning Groups |date=2004-09-05|publisher=Springer Berlin Heidelberg|isbn=978-3-540-23016-8|editor-last=Vreede|editor-first=Gert-Jan de|series=Lecture Notes in Computer Science|volume=3198 |pages=[https://archive.org/details/unset0000unse_i0a6/page/289 289–304]|language=en|doi=10.1007/978-3-540-30112-7_25|editor-last2=Guerrero|editor-first2=Luis A.|editor-last3=Raventós|editor-first3=Gabriela Marín|hdl=2117/116654|s2cid=6605 |chapter-url-access=registration|chapter-url=https://archive.org/details/unset0000unse_i0a6/page/289}}</ref> Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.<ref name=autogenerated1/>

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 (social science)|triangulation]], which will lead to an increase of evaluation [[Reliability (statistics)|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.<ref>{{Cite journal|last=Johnson|first=Karen E.|date=1996-01-01|title=Review of The Art of Case Study Research|jstor=329758|journal=The Modern Language Journal|volume=80|issue=4|pages=556–557|doi=10.2307/329758}}</ref>
** ''[[Ethnography|Ethnographic]] data'' such as student questionnaires and interviews and classroom non-participant observations<ref name=autogenerated1>{{cite journal|author1=Martínez, A.|author2=Dimitriadis, Y.|author3=Rubia, B.|author4=Gómez, E.|author5=de la Fuente, P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education. Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474|s2cid=10636524 }}</ref>
** ''[[Case study|Case studies]]'': comprehensively study particular CSCL situations and relate findings to general schemes<ref name=autogenerated1 />
** ''[[Content analysis]]:'' offers information about the content of the communication among members<ref name=autogenerated1 />
* 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 [[Logfile|log files]]:'' provide automatic data on how collaborative tools are used by learners<ref name=autogenerated1 />
** ''[[Multidimensional scaling|Multidimensional scaling (MDS)]]'': charts similarities among actors, so that more similar input data is closer together<ref name=autogenerated1 />
** ''[[Software]] tools:'' QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST<ref name=autogenerated1 />

==See also==
{{div col|colwidth=20em}}
* [[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]]
* [[Mosaic effect]]
* [[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]]
{{div col end}}

==References==
{{reflist|colwidth=35em|refs=
<ref name="asanet">{{cite journal |last1=Moody |first1=James |last2=White |first2=Douglas R. |title=Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups |journal=American Sociological Review |date=February 2003 |volume=68 |issue=1 |page=103 |doi=10.2307/3088904 |citeseerx=10.1.1.18.5695 |jstor=3088904 |s2cid=142591846 }}</ref>
<ref name="comprehensive">The most comprehensive reference is: {{cite book|author1=Wasserman, Stanley|author2=Faust, Katherine|name-list-style=amp|year=1994|title=Social Networks Analysis: Methods and Applications|location=Cambridge|publisher=Cambridge University Press|url=https://books.google.com/books?isbn=0521387078}} A short, clear basic summary is in {{cite journal|author1=Krebs, Valdis|author-link=Valdis Krebs|year=2000|title=The Social Life of Routers|journal=Internet Protocol Journal|volume=3 (December)|pages=14–25}}</ref>
<ref name="development">{{cite book|author=Linton Freeman|title=The Development of Social Network Analysis|location=Vancouver|publisher=Empirical Press|year=2006}}</ref>
<ref name="interpreting">{{cite journal |last1=McGrath |first1=Cathleen |last2=Blythe |first2=Jim |last3=Krackhardt |first3=David |title=The effect of spatial arrangement on judgments and errors in interpreting graphs |journal=Social Networks |date=August 1997 |volume=19 |issue=3 |pages=223–242 |doi=10.1016/S0378-8733(96)00299-7 |citeseerx=10.1.1.121.5856 }}</ref>
<ref name="uci">[http://intersci.ss.uci.edu/wiki/index.php/Cohesive_blocking 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.</ref>
<ref name="visualizing">{{cite journal |last1=Hogan |first1=Bernie |last2=Carrasco |first2=Juan Antonio |last3=Wellman |first3=Barry |title=Visualizing Personal Networks: Working with Participant-aided Sociograms |journal=Field Methods |date=May 2007 |volume=19 |issue=2 |pages=116–144 |doi=10.1177/1525822X06298589 |s2cid=61291563 }}</ref>
<ref name="Kadu12">{{cite book|author=Kadushin, C.|year=2012|title=Understanding social networks: Theories, concepts, and findings|location= Oxford|publisher=Oxford University Press|url=https://books.google.com/books?id=ALOhpMgkW_cC|isbn=978-0-19-537947-1}}</ref>
<ref name="Podo97">{{cite journal |last1=Podolny |first1=Joel M. |last2=Baron |first2=James N. |title=Resources and Relationships: Social Networks and Mobility in the Workplace |journal=American Sociological Review |date=October 1997 |volume=62 |issue=5 |page=673 |doi=10.2307/2657354 |jstor=2657354 |citeseerx=10.1.1.114.6822 }}</ref>
<ref name="Flyn10">{{cite journal |last1=Flynn |first1=Francis J. |last2=Reagans |first2=Ray E. |last3=Guillory |first3=Lucia |title=Do you two know each other? Transitivity, homophily, and the need for (network) closure. |journal=Journal of Personality and Social Psychology |date=2010 |volume=99 |issue=5 |pages=855–869 |doi=10.1037/a0020961 |pmid=20954787 |s2cid=6335920 }}</ref>
<ref name="Golbeck">{{cite book|author1=Golbeck, J.|year=2013|title=Analyzing the Social Web|publisher= Morgan Kaufmann|isbn=978-0-12-405856-9}}</ref>
}}

==External links==
{{Commons category|Social network analysis}}
<!--This page is for social networks only. Please post links to [[Social Networking websites]] and [[Social Network Service]] on those articles only. -->
<!--======================== {{No more links}} ============================
    | PLEASE BE CAUTIOUS IN ADDING MORE LINKS TO THIS ARTICLE. Wikipedia  |
    | is not a collection of links nor should it be used for advertising. |
    |                                                                     |
    | Excessive or inappropriate links WILL BE DELETED.                   |
    | See [[Wikipedia:External links]] & [[Wikipedia:Spam]] for details.  |
    |                                                                     |
    | If there are already plentiful links, please propose additions or   |
    | replacements on this article's discussion page, or submit your link |
    | to the relevant category at the Open Directory Project (dmoz.org)   |
    | and link back to that category using the {{dmoz}} template.         |
============================ {{No more links}} =========================-->
* [http://www.insna.org/ International Network for Social Network Analysis]
* [https://github.com/briatte/awesome-network-analysis/ Awesome Network Analysis] – 200+ links to books, conferences, courses, journals, research groups, software, tutorials and more
* [http://netwiki.amath.unc.edu/ Netwiki] – wiki page devoted to social networks; maintained at University of North Carolina at Chapel Hill
* [https://web.archive.org/web/20110516133549/http://stat.gamma.rug.nl/SnijdersSteglichVdBunt2009.pdf Introduction to Stochastic Actor-Based Models for Network Dynamics – Snijders et al.]
* [http://www.casos.cs.cmu.edu Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon]
* [http://www.chass.utoronto.ca/~wellman/netlab/ABOUT/index.html NetLab at the University of Toronto, studies the intersection of social, communication, information and computing networks]
* [https://web.archive.org/web/20080215084223/http://www.ksg.harvard.edu/netgov/ Program on Networked Governance], Harvard University
* [https://web.archive.org/web/20110217151303/http://www.oeaw.ac.at/byzanz/historicaldynamics.htm 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)]
* [https://leb.fbi.gov/2013/march/social-network-analysis-a-systematic-approach-for-investigating Social Network Analysis: A Systematic Approach for Investigating]
* ''[https://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg
* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle
* ''[http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118169476.html Social Network Analysis with Applications]'' (2013) by I. McCulloh, H. Armstrong & A. Johnson
{{Social networking}}
{{Authority control}}

{{DEFAULTSORT:Social Network}}
[[Category:Social networks]]
[[Category:Value (ethics)]]
[[Category:Systems theory]]
[[Category:Social systems]]
[[Category:Self-organization]]
[[Category:Community building]]
[[Category:Cultural economics]]
[[Category:Social information processing]]
[[Category:Mass media monitoring]]
[[Category:Surveillance]]
[[Category:Types of analytics]]
[[Category:Methods in sociology]]
[[Category:Internet culture]]
[[Category:Social network analysis| ]]