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Reseach Article

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

by Sanjiv Sharma, G. N. Purohit
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 9
Year of Publication: 2013
Authors: Sanjiv Sharma, G. N. Purohit
10.5120/10498-5261

Sanjiv Sharma, G. N. Purohit . A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network. International Journal of Computer Applications. 63, 9 ( February 2013), 45-50. DOI=10.5120/10498-5261

@article{ 10.5120/10498-5261,
author = { Sanjiv Sharma, G. N. Purohit },
title = { A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 9 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number9/10498-5261/ },
doi = { 10.5120/10498-5261 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:13:55.213968+05:30
%A Sanjiv Sharma
%A G. N. Purohit
%T A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 9
%P 45-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social relationships and networking are key components of human life. Social network analysis provides both a visual and a mathematical analysis of human relationships. Recently, online social networks have gained significant popularity. This popularity provides an opportunity to study the characteristics of online social network graphs at large scale. An online social network graph consists of people as nodes who interact in some way such as members of online communities sharing information using relationships among them. In this paper a state of the art survey of the works done on community tracking in social network. The main goal is to provide a road map for researchers working on different measures for tracking communities in Social Network.

References
  1. Scott, Social Network Analysis. A Handbook. Sage (2000).
  2. Shuie, Yih-Chearng. Exploring and Mitigating Social Loafing in Online Communitie. Computers and Behavior. v. 26. 4, July 2010. p. 768–777
  3. WASSERMAN, S. & FAUST, K. Social Network Analysis: Methods and Applications, Cambridge University Press (1995).
  4. Kumar, R. , Raghavan, P. , Rajagopalan, S. , and Tomkins, A. Trawling the web for emerging cyber-communities. Computer Networks (1999).
  5. Flake, G. W. , Lawrence, S. , Giles, C. L. , and Coetzee, F. M. Selforganization and identification of web communities. IEEE Computer 35, 3 (2002),66–071.
  6. Chau, M. , Shiu, B. , Chan, I. , and Chen, H. Automated identification of web communities for business intelligence analysis. In Proceedings of the Fourth Workshop on E-Business (WEB) (New York, NY, USA,
  7. Gruzd, A. , and Haythornthwaite, C. Automated discovery and analysis of social networks from threaded discussions. Paper presented at the International Network of Social Network Analysts (2008).
  8. Gibson, D. , Kumar, R. , and Tomkins, A. Discovering large dense subgraphs in massive graphs. In VLDB '05: Proceedings of the 31st international conference on Very large data bases (2005), VLDB Endowment, pp. 721–732.
  9. Tantipathananandh, C. , Berger-Wolf, T. Y. , and Kempe, D. 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 (New York, NY, USA, 2007), ACM, pp. 717–726.
  10. Zhao, Q. , Liu, T. -Y. , and Ma, W. -Y. Predicting community members based on evolution of heterogeneous networks (patent number us 2007/0239677 a1). Microsoft Corporation, 2007.
  11. Joachims, T. Making large-scale svm learning practical. Advances in Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burgess and A. Smola (ed. ) (1999).
  12. Nie, Z. , Zhang, Y. , Wen, J. -R. , and Ma, W. -Y. Object-level ranking: bringing order to web objects. In WWW '05: Proceedings of the 14th international conference on World Wide Web (New York, NY, USA, 2005), ACM, pp. 567–574.
  13. Fisher, D. Using egocentric networks to understand communication. IEEE Internet Computing 9, 5 (2005), 20–28.
  14. Frivolt, G. , and Bielikov, M. An approach for community cutting. In RAWS 2005 Proc. of the 1st Int. Workshop on Representation and Analysis of Web Space, V. Svatek, V. Snasel (Eds. ) (2005), pp. 49–54.
  15. Chin, A. , and Chignell, M. 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 (Odense, Denmark, 2006), ACM, pp. 11–22.
  16. Frivolt, G. , and Bielikov, M. An approach for community cutting. In RAWS 2005 Proc. of the 1st Int. Workshop on Representation and Analysis of Web Space, V. Svatek, V. Snasel (Eds. ) (2005), pp. 49–54.
  17. Ma, H. -W. , and Zeng, A. -P. The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19, 11 (2003), 1423–1430.
  18. Donetti, L. , and Munoz, M. A. Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment 2004, 10 (2004), P10012.
  19. Girvan, M. , and Newman, M. E. Community structure in social and biological networks. PROC. NATL. ACAD. SCI. USA 99 (2002), 7821.
  20. Gloor, P. A. , Laubacher, R. , Dynes, S. B. C. , and Zhao, Y. Visualization of communication patterns in collaborative innovation networks - analysis of some w3c working groups. In CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management (New York, NY, USA, 2003), ACM Press, pp. 56–60.
  21. Costenbader, E. , and Valente, T. W. The stability of centrality measures when networks are sampled. Social Networks 25 (Oct. 2003), 283–307.
  22. Crucitti, P. , Latora, V. , and Porta, S. Centrality measures in spatial networks of urban streets. Physical Review E 73 (2006), 036125.
  23. Estrada, E. , and Rodriguez-Velazquez, J. A. Subgraph centrality in complex networks. Physical Review E 71 (2005), 056103.
  24. Newman, M. E. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 23 (2006), 8577–8582.
  25. Memon, N. , Harkiolakis, N. , and Hicks, D. Detecting high-value individuals in covert networks: 7/7 london bombing case study. Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on (31 2008-April 4 2008), 206–215.
  26. Memon, N. , Larsen, H. L. , Hicks, D. L. , and Harkiolakis, N. Detecting hidden hierarchy in terrorist networks: Some case studies. Lecture Notes in Computer Science 5075 (2008), 477–489.
  27. Costenbader, E. , and Valente, T. W. The stability of centrality measures when networks are sampled. Social Networks 25 (Oct. 2003), 283–307.
  28. Duda, R. O. , Hart, P. E. , and Stork, D. G. Unsupervised Learning and Clustering. Wiley, New York, 2001.
  29. Alba, R. D. A graph-theoretic definition of a sociometric clique. Journal of Mathematical Sociology 3 (2003), 113–126.
  30. Balasundaram, B. , Butenko, S. , Hicks, I. , and Sachdeva, S. Clique relaxations in social network analysis: The maximum k-plex problem. Tech. rep. , Texas A and M Engineering, 2007.
  31. Chin, A. , and Chignell, M. Identifying subcommunities using cohesive subgroups in social hypertext. In HT '07: Proceedings of the eighteenth conference on Hypertext and hypermedia (New York, NY, USA, 2007), ACM, pp. 175–178.
  32. Brooks, C. H. , and Montanez, N. Improved annotation of the blogosphere via autotagging and hierarchical clustering. In WWW '06: Proceedings of the 15th international conference on World Wide Web (New York, NY, USA, 2006), ACM Press, pp. 625–632.
  33. Li, X. , Liu, B. , and Yu, P. S. Mining community structure of named entities from web pages and blogs. In AAAI Spring Syposium-2006 (2006), AAAI.
  34. G´omez, V. , Kaltenbrunner, A. , and L´opez, V. Statistical analysis of the social network and discussion threads in slashdot. In WWW '08: Proceeding of the 17th international conference on World Wide Web (New York, NY, USA, 2008), ACM, pp. 645–654.
  35. Johnson, S. C. Hierarchical clustering schemes. Psychometrika 32 .
  36. Hartigan, J. Clustering Algorithms. John Wiley and Sons, New York, NY, 1975.
  37. Orford, J. D. Implementation of criteria for partitioning a dendrogram. Mathematical Geology 8, 1 (1976), 75–84.
  38. Noack, A. Modularity clustering is force-directed layout. Retrieved from http://www. citebase. org/abstract?id=oai:arXiv. org:0807. 4052 [accessed 30 September 2008], 2008.
  39. Radicchi, F. , Castellano, C. , Cecconi, F. , Loreto, V. , and Parisi, D. vDefining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 101, 9 (2004), 2658–2663.
  40. van Duijn, M. A. J. , and Vermunt, J. K. What is special about social network analysis? Methodology 2 (2005), 2–6.
  41. Elmore, K. L. , and Richman, M. B. Euclidean distance as a similarity metric for principal component analysis. Monthly Weather Review 129, 3 (March 2001), 540–549.
  42. Santini, S. , and Jain, R. Similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 9 (September 1999), 871–883.
  43. Tversky, A. Features of similarity. Psychological Review 84, 4 (1977), 327–352.
  44. Jaccard, P. Distribution de la flore alpine dans le bassin des dranses et dans quelques rgions voisines. Bulletin del la Socit Vaudoise des Sciences Naturellese 37 (1901), 241–272.
  45. Falkowski, T. , Bartelheimer, J. , and Spiliopoulou, M. Community dynamics mining. In Proceedings of 14th European Conference on Information Systems (ECIS 2006) (Gteborg, Sweden, 2006).
  46. Leskovec, J. , Lang, K. J. , Dasgupta, A. , and Mahoney, M. W. Statistical properties of community structure in large social and information networks. In WWW '08: Proceeding of the 17th international conference on World Wide Web (New York, NY, USA, 2008), ACM, pp. 695–704.
  47. Hirsch, B. J. Psychological dimensions of social networks: A multimethod analysis. American Journal of Community Psychology 7, 3 (1979), 263–277.
  48. Sarason, I. G. , Levine, H. M. , Basham, R. B. , and Sarason, B. R. Assessing social support: The social support questionnaire. Journal of Personality and Social Psychology 44 (1983), 127–139.
  49. Chin A, Chignell M Automatic detection of cohesive subgroups within social hypertext:
  50. A heuristic approach. New Rev Hypermed Multimed (2008) 14(1):121–143
  51. Tajfel, H. , and Turner, J. C. The social identity theory of inter-group behavior. In S. Worchel and L. W. Austin (eds. ), Psychology of Intergroup Relations (1986).
  52. Chin A, Chignell M ,Wang H(2010) Tracking cohesive subgroup over time in inferred social network. In New Review of Hypermedia and Multimedia / Hypermedia , vol. 16, no. 1&2, (2010) pp. 113-139,
Index Terms

Computer Science
Information Sciences

Keywords

Social network community graph measures analysis