Abstract
This chapter examines the problem of tracking community in social networks inferred from online interactions by tracking evolution of known subgroups over time. Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. A variety of approaches have been suggested to address this problem and the corresponding research literature on centrality, clustering, and optimization methods for finding subgroupings is reviewed. This review will include a critical analysis of the limitations of past approaches. The focus of the chapter will then turn to novel methods for tracking online community interaction. First, the method proposed by Chin and Chignell called SCAN will be briefly introduced, where a combination of heuristic methods is used to identify subgroups in a manner that can potentially scale up to very large social networks. Then, we present the DISSECT method where multiple known subgroups within a social network are tracked in terms of similarity-based cohesiveness over time. The DISSECT method relies on cluster analysis of snapshots of network activity at different points in time followed by similarity analysis of subgroup evolution over successive time periods. The DISSECT method can be supplemented with behavioral measures of sense of community where administration of a questionnaire is feasible. Finally, we conclude the chapter with a discussion on possible applications and use of the DISSECT method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adar E, Li Z, Adamic LA, Lukose RM (May 2004) Implicit structure and the dynamics of blogspace. In: Workshop on the weblogging ecosystem, 13th international World Wide Web conference
Alba RD (2003) A graph-theoretic definition of a sociometric clique. J Math Sociol 3:113–126
Anderson CJ, Wasserman S, Faust K (1997) Building stochastic blockmodels. Social Networks 14:137–161
Backstrom L (2006) Group formation in large social networks: membership, growth, and evolution. In: KDD 06: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, ACM Press, pp 44–54
Balasundaram B, Butenko S, Hicks I, Sachdeva S (2007) Clique relaxations in social network analysis: the maximum k-plex problem. Technical report, Texas A and M Engineering
Bass LA, Stein CH (1997) Comparing the structure and stability of network ties using the social support questionnaire and the social network list. J Soc Pers Relat 14:123–132
Bird C (2006) Community structure in oss projects. Technical report, University of California, Davis
Blanchard AL, Markus ML (2004) The experienced “sense” of a virtual community: characteristics and processes. SIGMIS Database 35(1):64–79
Borgatti SP, Everett GM, Freeman CL (2002) Ucinet for windows: software for social network analysis. Analytic Technologies, Harvard, USA
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: WWW7: Proceedings of the 7th international conference on World Wide Web 7. Elsevier Science BV, Amsterdam, the Netherlands, pp 107–117
Burt R (1982) Toward a structural theory of action: network models of social structure, perception, and action. Academic, New York
Burt R (1984) Network items and the general social survey. Social Networks 6:293–339
Campbell KE, Barret AL (1991) Name generators in surveys of personal networks. Social Networks 13:203–221
Carrington PJ, Scott J, Wasserman S (2006) Models and methods in social network analysis. Cambridge University Press, New York, NY, USA
Cervini AL (2003) Network connections: An analysis of social software that turns online introductions into offline interactions. Master’s thesis, New York University, New York, NY
Chavis DM (2008) Sense of community index. http://www.capablecommunity.com/pubs/Sense\%20of\%20Community\%20Index.pdf. Accessed 30 September 2008
Chavis DM, Wandersman A (1990) Sense of community in the urban environment: a catalyst for participation and community development. Am J Commun Psychol 18(1):55–81
Chin A (January 2009) Social cohesion analysis of networks: a method for finding cohesive subgroups in social hypertext. PhD thesis, University of Toronto
Chin A, Chignell M (2006) A social hypertext model for finding community in blogs. In: Proceedings of the 17th international ACM conference on hypertext and hypermedia: tools for supporting social structures. ACM, Odense, Denmark, pp 11–22
Chin A, Chignell M (2007) Identifying communities in blogs: roles for social network analysis and survey instruments. Int J Web Based Commun 3(3):345–363
Chin A, Chignell M (2007) Identifying subcommunities using cohesive subgroups in social hypertext. In: HT ’07: Proceedings of the 18th conference on hypertext and hypermedia. ACM, New York, NY, USA, pp 175–178
Chin A, Chignell M (2008) Automatic detection of cohesive subgroups within social hypertext: A heuristic approach. New Rev Hypermed Multimed 14(1):121–143
Chin A, Keelan J, Pavri-Garcia V, Tomlinson G, Wilson K, Chignell M (2009) Automated delineation of subgroups in web video: A medical activism case study. Journal of Computer-Mediated Communication. In Press
Clauset A (2005) Finding local community structure in networks. Phys Rev E 72:026132
Costenbader E, Thomas WV (October 2003) The stability of centrality measures when networks are sampled. Social Networks 25:283–307
Crucitti P, Latora V, Porta S (2006) Centrality measures in spatial networks of urban streets. Phys Rev E 73:036125
Danon L, Duch J, Diaz-Guilera A, Arenas A (2005) Comparing community structure identification. J Stat Mech Theor Exp: P09008
de Nooy W, Mrvar A, Batagelj V (2005) Exploratory social network analysis with Pajek. Cambridge University Press, New York, USA
Dixon J (1981) Towards an understanding of the implications of boundary changes – with emphasis on community of interest, draft report to the rural adjustment unit. Technical report, University of New England, Armidale
Donetti L, Munoz AM (2004) Detecting network communities: a new systematic and efficient algorithm. J Stat Mech Theor Exp 2004(10):P10012
Driskell BR, Lyon L (2002) Are virtual communities true communities? Examining the environments and elements of community. City and Community 1(4):373–390
Du N, Wu B, Pei X, Wang B, Xu L (2007) Community detection in large-scale social networks. In WebKDD/SNA-KDD ’07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, New York, NY, USA, pp 16–25
Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E (Stat Nonlinear Soft Matter Phys) 72(2):027104
Dwyer T, Hong HS, Koschutzki D, Schreiber F, Xu K (2006) Visual analysis of network centralities. In: APVis ’06: Proceedings of the 2006 Asia-Pacific symposium on information visualisation. Australian Computer Society, Darlinghurst, Australia, pp 189–197
Elmore LK, Richman BM (March 2001) Euclidean distance as a similarity metric for principal component analysis. Month Weather Rev 129(3):540–549
Erickson T (1996) The world-wide-web as social hypertext. Commun ACM 39(1):15–17
Estrada E, Rodriguez-Velazquez AJ (2005) Subgraph centrality in complex networks. Phys Rev E 71:056103
Etzioni A, Etzioni O (2001) Can virtual communities be real? In: Etzioni A (ed) The Monochrome Society, Princeton University Press, Princeton, pp 77–101
Falkowski T, Bartelheimer J, Spiliopoulou M (2006) Community dynamics mining. In: Proceedings of 14th European conference on information systems (ECIS 2006). Gteborg, Sweden
Fisher D (2005) Using egocentric networks to understand communication. IEEE Internet Comput 9(5):20–28
Flake WG, Lawrence S, Giles LC, Coetzee MF (2002) Self-organization and identification of web communities. IEEE Computer 35(3):66–71
Fortunato S, Latora V, Marchiori M (2004) Method to find community structures based on information centrality. Phys Rev E (Stat Nonlinear, Soft Matter Phys) 70(5):056104
Frank AK (1997) Identifying cohesive subgroups. Social Networks 17(1):27–56
Freeman CL (1978) Centrality in social networks: Conceptual clarification. Social Networks 1:215–239
Frivolt G, Bielikov M (2005) An approach for community cutting. In: Svatek V, Snasel V (eds) RAWS 2005: Proceedings of the 1st International workshop on representation and analysis of Web space, Prague-Tocna, Czech Republic, pp 49–54
Garton L, Haythornthwaite C, Wellman B (1997) Studying online social networks. J Comput Mediated Commun 3(1):1–30
Girvan M, Newman EJM (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821
Gloor AP (2005) Capturing team dynamics through temporal social surfaces. In: Proceedings of the 9th international conference on information visualisation (InfoVis 2005). IEEE, pp 939–944
Gloor AP, Laubacher R, Dynes BCS, Zhao Y (2003) Visualization of communication patterns in collaborative innovation networks – analysis of some w3c working groups. In: CIKM ’03: Proceedings of the 12th international conference on information and knowledge management, ACM Press, New York, NY, USA, pp 56–60
Gómez V, Kaltenbrunner A, López V (2008) Statistical analysis of the social network and discussion threads in slashdot. In: WWW ’08: Proceedings of the 17th international conference on World Wide Web. ACM, New York, NY, USA, pp 645–654
Gregson AMR (1975) Psychometrics of similarity. Academic, NY, USA
Gruzd A, Haythornthwaite C (2007) A noun phrase analysis tool for mining online community. In: Proceedings of the 3rd international conference oncommunities and technologies, East Lansing, Michigan, USA, pp 67–86
Gruzd A, Haythornthwaite C (2008) Automated discovery and analysis of social networks from threaded discussions. Paper presented at the International Network of Social Network Analysis, St. Pete Beach, FL, USA
Hanneman AR, Riddle M (2005) Introduction to social network methods (online textbook). University of California, Riverside, CA
Hartigan J (1975) Clustering algorithms. Wiley, New York, NY, USA
Hirsch JB (1979) Psychological dimensions of social networks: A multimethod analysis. Am J Commun Psychol 7(3):263–277
Hoskinson A (2005) Creating the ultimate research assistant. Computer 38(11):97–99
Hubert JL, Schultz J (1976) Quadratic assignment as a general data analysis strategy. Brit J Math Stat Psychol 29:190–241
Jaccard P (1901) Distribution de la flore alpine dans le bassin des dranses et dans quelques rgions voisines. Bulletin del la Socit Vaudoise des Sciences Naturellese, 37:241–272
Johnson CS (1967) Hierarchical clustering schemes.Psychometrika, 32
Jones Q (1997) Virtual-communities, virtual settlements and cyber-archaeology: A theoretical outline. J Comput Supported Coop Work 3(3)
Jung Y, Park H, Du DZ, Drake LB (2003) A decision criterion for the optimal number of clusters in hierarchical clustering. J Global Optim 25(1):91–111
Keelan J, Pavri-Garcia V, Tomlinson G, Wilson K (2007) Youtube as a source of information on immunization: a content analysis. JAMA: J Am Med Assoc 298(21):2482–2484
Kleinberg J (2002) Bursty and hierarchical structure in streams. In: KDD ’02: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA, pp 91–101
Kleinberg MJ (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5): 604–632
Koschtzki D, Schreiber F (2004) Comparison of centralities for biological networks.In: Giegerich R, Stoye J (eds) Proceedings of the German conference on bioinformatics (GCB’04), Bielefield, Germany, pp 199–206
Kumar R, Novak J, Raghavan P, Tomkins A (2003) On the bursty evolution of blogspace. In: WWW ’03: Proceedings of the 12th international conference on World Wide Web. ACM, New York, NY, USA, pp 568–576
Kumar R, Novak J, Raghavan P, Tomkins A (2004) Structure and evolution of blogspace. Commun ACM 47(12):35–39
Kumar R, Raghavan P, Rajagopalan S, Tomkins A (1999) Trawling the web for emerging cyber-communities. Computer Networks 31(11–16), pp 1481–1493
Kurdia A, Daescu O, Ammann L, Kakhniashvili D, Goodman RS (November 2007) Centrality measures for the human red blood cell interactome. Engineering in Medicine and Biology Workshop. IEEE, Dallas, pp 98–101
Leskovec J, Lang JK, Dasgupta A, Mahoney WM (2008) Statistical properties of community structure in large social and information networks. In: WWW ’08: Proceedings of the 17th international conference on World Wide Web. ACM, New York, NY, USA, pp 695–704
Leydesdorff L, Schank T, Scharnhorst A, de Nooy W (2008) Animating the development of social networks over time using a dynamic extension of multidimensional scaling
Li X, Liu B, Yu SP (2006) Mining community structure of named entities from web pages and blogs. In: AAAI Spring Symposium Series. American Association for Artificial Intelligence
Lin RY, Chi Y, Zhu S, Sundaram H, Tseng LB (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: WWW ’08: Proceedings of the 17th international conference on World Wide Web. ACM, New York, NY, USA, pp 685–694
Ma W-H, Zeng PA (2003) The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19(11):1423–1430
Marlow C (2004) Audience, structure and authority in the weblog community. In: International communication association conference, New Orleans, LA
McMillan WD, Chavis DM (1986) Sense of community: a definition and theory. J Commun Psychol 14(1):6–23
Memon N, Harkiolakis N, Hicks LD (2008) Detecting high-value individuals in covert networks: 7/7 London bombing case study. In Proceedings of the 2008 IEEE/ACS International Conference on computer systems and applications. IEEE Computer Society, Washington DC, USA, 4–31 April 2008, pp 206–215
Memon N, Larsen LH, Hicks LD, Harkiolakis N (2008) Detecting hidden hierarchy in terrorist networks: Some case studies. Lect Notes Comput Sci 5075:477–489
Mizruchi SM, Mariolis P, Schwartz M, Mintz B (1986) Techniques for disaggregating centrality scores in social networks. Sociol Methodol 16:26–48
Moody J, McFarland AD, Bender-deMoll S (2005) Visualizing network dynamics. Am J Sociol: Jan 2005
Mukherjee M, Holder LB (2004) Graph-based data mining on social networks. In: Proceedings of the 10th ACM SIG conference on knowledge discovery and data mining, ACM, Seattle, USA, pp 1–10
Neustaedter C, Brush AJ, Smith AM, Fisher D (2005) The social network and relationship finder: Social sorting for email triage. In: Proceedings of the 2nd conference on E-mail and anti-spam (CEAS 2005), California, USA
Newman EJM (2006) Modularity and community structure in networks. Proc Nat Acad Sci 103(23):8577–8582
Newman EJM, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113
O’Reilly T (2005) What is web 2.0? http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20. Accessed 30 September 2008
Orford DJ (1976) Implementation of criteria for partitioning a dendrogram. Math Geol 8(1):75–84
Paolillo CJ, Wright E (2004) The challenges of foaf characterization. http://stderr.org/~elw/foaf/. Accessed 30 September 2008
Paolillo CJ, Wright E (2005) Social network analysis on the semantic web: Techniques and challenges for visualizing foaf. http://www.blogninja.com/vsw-draft-paolillo-wright-foaf.pdf. Accessed 30 September 2008
Piper EW, Marrache M, Lacroix R, Richardsen MA, Jones BD (1983) Cohesion as a basic bond in groups. Hum Relat 36(2):93–108
Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101(9):2658–2663
Reffay C, Chanier T (2003) How social network analysis can help to measure cohesion in collaborative distance learning. In: Proceedings of computer supported collaborative learning 2003. Kluwer, ACM, Dordrecht, NL, pp 343–352
Rheingold H (1993) The virtual community: homesteading on the electronic frontier. Addison-Wesley, Toronto, ON, Canada
Ruan J, Zhang W An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In: Seventh IEEE international conference on data mining (ICDM 2007), Omaha, Nebraska, USA, 28–31 October 2007, pp 643–648
Ruhnau B (October 2000) Eigenvector-centrality – a node-centrality? Social Networks 22(4):357–365
Sarason GI, Levine HM, Basham BR, Sarason RB (1983) Assessing social support: the social support questionnaire. J Pers Social Psychol 44:127–139
Schaeffer ES (2007) Graph clustering. Comput Sci Rev 1(1):27–64
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Snijders ABT, Nowicki K (1997) Estimation and prediction for stochastic block models for graphs with latent block structure. J Classif 14:75–100
Snijders AB Tom, Christian EG Steglich, Schweinberger M (2007) Modeling the co-evolution of networks and behavior. In: Kees van Montfort, Han Oud, Albert Satorra (eds) Longitudinal models in the behavioral and related sciences, Routledge Academic, England, pp 41–71
Steinhaeuser K, Chawla VN (2008) Is modularity the answer to evaluating community structure in networks. In: International workshop and conference on network science (NetSci’08), Norwich Research Park, UK
Sterling S (2004) Aggregation techniques to characterize social networks. Master’s thesis, Air Force Institute of Technology. Ohio, USA
Tajfel H, Turner CJ (1986) The social identity theory of inter-group behavior. In: Worchel S, Austin LW (eds) Psychology of intergroup relations. Nelson-Hall, Chicago, USA
Tantipathananandh C, Berger-Wolf YT, Kempe D (2007) A framework for community identification in dynamic social networks. In: KDD ’07: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, NY, USA, pp 717–726
Traud LA, Kelsic DE, Mucha JP, Porter AM (2009) Community structure in online collegiate social networks, American Physical Society, 2009 APS March Meeting, March 16–20, pp 1–38
Tremayne M, Zheng N, Lee KJ, Jeong J (2006) Issue publics on the web: Applying network theory to the war blogosphere. J Comput Mediated Commun 12(1), article 15. http://jcmc.indiana.edu/vol12/issue1/tremayne.html
Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352
Tyler RJ, Wilkinson MD, Huberman AB (2005) E-mail as spectroscopy: Automated discovery of community structure within organizations. Inform Soc 21(2):143–153
Uttal RW, Spillmann L, Sturzel F, Sekuler BA (2000) Motion and shape in common fate. Vision Res 40(3):301–310
van Duijn1 AJM, Vermunt KJ (2005) What is special about social network analysis? Methodology 2:2–6
Wang G, Shen Y, Ouyang M (2008) A vector partitioning approach to detecting community structure in complex networks. Comput Math Appl 55(12):2746–2752
Wang H, Wang W, Yang J, Yu SP (2002) Clustering by pattern similarity in large data sets. In: SIGMOD ’02: Proceedings of the 2002 ACM SIGMOD international conference on management of data. ACM, New York, NY, USA, pp 394–405
Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, United Kingdom
Wellman B (2003) Structural analysis: from method and metaphor to theory and substance. In: Wellman B, Berkowitz SD (eds) Social structures: a network approach, Cambridge University Press, UK, pp 19–61
Wellman B, Guilia M (1999) Net surfers don’t ride alone: virtual communities as communities. In: Wellman B (ed) Networks in the global village: life in contemporary communities, Westview Press, Colorado, US
Welser TH, Gleave E, Fisher D, Smith M (2007) Visualizing the signatures of social roles in online discussion groups. J Soc Struct 8, http://www.cmu.edu/joss/content/articles/volume8/Welser
Zahn TC (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput C-20(1):68–86
Zhao Y, Karypis G (2002) Evaluation of hierarchical clustering algorithms for document datasets. In: CIKM ’02: Proceedings of the 11th international conference on information and knowledge management. ACM, New York, NY, USA, pp 515–524
Acknowledgements
We would like to thank the TorCamp group for allowing us to use their Google Groups site for data analysis and the participants for completing the behavioral surveys. The authors would also like to thank Jennifer Keelan and Kumanan Wilson for providing us with the content analysis information from the YouTube vaccination videos shown in Table 4.1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag London Limited
About this chapter
Cite this chapter
Chin, A., Chignell, M. (2010). DISSECT: Data-Intensive Socially Similar Evolving Community Tracker. In: Abraham, A., Hassanien, AE., Sná¿el, V. (eds) Computational Social Network Analysis. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-229-0_4
Download citation
DOI: https://doi.org/10.1007/978-1-84882-229-0_4
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-84882-228-3
Online ISBN: 978-1-84882-229-0
eBook Packages: Computer ScienceComputer Science (R0)