Social network analysis
|Part of a series on|
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. 
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).    
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.
Size: The number of network members in a given network.
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.
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. 
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.
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. 
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.  
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 (SNP) is a numeric coefficient, derived through algorithms   to represent both the size of an individual's social network and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is Alpha User, defined as a person with a high SNP.
SNP coefficients have two primary functions:
- The classification of individuals based on their social networking potential, and
- The weighting of respondents in quantitative marketing research studies.
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.
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.  
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. 
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the Semantic Brand Score). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).
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 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. 
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.
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. 
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. 
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 
- Actor-network theory
- 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
- Attention inequality
- 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 & Humanities. 3 (1): 1171458. doi: 10.1080/23311983.2016.1171458.
- 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 Società delle Nazioni]. Memoria e Ricerca (2): 371–393. doi: 10.14647/87204. See also: French version (PDF) and English summary.
- 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.
- 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.
- 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.
- Pinheiro, Carlos A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & 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.
- Social network differences of chronotypes identified from mobile phone data. 2018. OCLC 1062367169.
- 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.
- 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.
- 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, Michał B.; Jarynowski, Andrzej; Jelińska, 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.
- 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 literêre 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. & 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.
- 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. & 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 & 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 & 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. & 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 & 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. & 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 & 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 & 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., & 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., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & 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., & Melkko, S. (2005). 3G marketing: communities and strategic partnerships. John Wiley & 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.
- Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010
- Sudhahar S, Veltri GA, Cristianini N (2015). "Automated analysis of the US presidential elections using Big Data and network analysis". Big Data & 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.
- 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. & Hakkarainen, K. B. Fishman & 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.
- Martínez, A.; Dimitriadis, Y.; Rubia, B.; Gómez, E.; de la Fuente, P. (2003-12-01). "Combining qualitative evaluation and social network analysis for the study of classroom social interactions". Computers & 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. & 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. & 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; Martínez-Monés, Alejandra; Xhafa, Fatos (2004-09-05). Vreede, Gert-Jan de; Guerrero, Luis A.; Raventós, Gabriela Marín (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.
- Martínez, A.; Dimitriadis, Y.; Rubia, B.; Gómez, E.; de la Fuente, P. (2003-12-01). "Combining qualitative evaluation and social network analysis for the study of classroom social interactions". Computers & 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.
This article's use of external links may not follow Wikipedia's policies or guidelines. (January 2017)
- 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 Investigating
- 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 Autónoma de Barcelona y Universidad de Sevilla. Cite journal requires
"Connections". International Network for Social Network Analysis. Archived from
the original on 2013-07-18. Cite journal requires
- Networks, Crowds, and Markets (2010) by D. Easley & J. Kleinberg
- Introduction to Social Networks Methods (2005) by R. Hanneman & M. Riddle
- Social Network Analysis with Applications (2013) by I. McCulloh, H. Armstrong & A. Johnson
- Social Network Analysis in Telecommunications (2011) by Carlos Andre Reis Pinheiro
|Wikimedia Commons has media related to Social network analysis.|