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A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks

by Renuga Devi. R, Hemalatha. M
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 3
Year of Publication: 2013
Authors: Renuga Devi. R, Hemalatha. M
10.5120/13089-0368

Renuga Devi. R, Hemalatha. M . A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks. International Journal of Computer Applications. 75, 3 ( August 2013), 7-12. DOI=10.5120/13089-0368

@article{ 10.5120/13089-0368,
author = { Renuga Devi. R, Hemalatha. M },
title = { A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 3 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number3/13089-0368/ },
doi = { 10.5120/13089-0368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:15.789751+05:30
%A Renuga Devi. R
%A Hemalatha. M
%T A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 3
%P 7-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Research in social network analysis has increased in recent years. Because of the popularity of the social networking sites, many researchers concentrate on this area for research. In this, community mining plays an important role. Hidden communities affect the social networks in different ways. But not all hidden communities are dangerous or illegal. Most of the hidden communities are having potential knowledge. Communities are represented as a graph format. People are represented as nodes, and the relationship between the nodes are represented as edges. Several mining techniques do not considered the disconnected edges in the graph. Those hidden or disconnected edges may useful to the others in the network. Our approach on social network is fully based on the community mining on heterogeneous network. Here we analysis the various community mining techniques which is already available. Such as MinCut algorithm, Regression based algorithm, Max-Min modularity measure, LM algorithm and SECI model. Our results show that, there are some limitations in the hidden community mining technique in large scale networks. So we planned to do research in this area for better improvement.

References
  1. Deng Cai, Zheng Shao, Xiaofei He, Xifeng Yan, and Jiawei Han, Mining Hidden Community in Heterogeneous Social Networks, Report No. UIUCDCS-R-2005-2538 UILU-ENG-1731, March 2005.
  2. Osmar R. Za¨?ane, Jiyang Chen, and Randy Goebel, Mining Research Communities in Bibliographical Data , University of Alberta, Canada.
  3. Bo Yang, Jiming Liu, and Jianfeng Feng, On the Spectral Characterization and Scalable Mining of Network Communities. IEEE Transactions on Knowledge and Data Engineering. VOL. 24, No. 2, pp. 1041-4347, 2012.
  4. M. Fiedler, Algebraic Connectivity of Graphs, Czechoslovakian Math. J. , vol. 23, pp. 298-305, 1973.
  5. Richard Ribeiro and chris kimble,. Identifying 'Hidden' Communities of Practice Within Electronic Networks: Some Preliminary Premises, In Proceedings of 13th UKAIS Conference, Bournemouth, UK, 2008
  6. M. E. J. Newman, Modularity and Community Structure in Networks, Proc. Nat'l Academy of Sciences USA, vol. 103, no. 23, pp. 8577-8582, 2006.
  7. S. White and P. Smyth, A Spectral Clustering Approach to Finding Communities in Graphs, Proceeding of Fifth SIAM Int'l Conf. Data Mining, 2005.
  8. M. Shiga, I. Takigawa, and H. Mamitsuka, A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network, Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 647-656, 2007.
  9. L. Getoor and C. P. Diehl. Link mining: a survey. SIGKDD Explor. Newsl. , 7(2):3-12, 2005.
  10. Lave, J and Wenger. E. Situated learning:Legitimate peripheral participation, Cambridge University Press, 1991.
  11. M. F. Schwartz and D. C. M. Wood, Discovering shared interests using graph analysis. Communications of the ACM, 36(8):78–89, 1993.
  12. P. Domingos and M. Richardson, Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 57–66. ACM Press, 2001.
  13. Richard Ribeiro and Chris Kimble, Identifying 'Hidden' Communities of Practice with in electronic networks: Some preliminary premises, 13th UKAIS conference (UKAIS). 2008.
  14. C. H. Q. Ding, X. He, H. Zha, M. Gu, and H. D. Simon, A min-max cut algorithm for graph partitioning and data clustering. In ICDM, pages 107-114, 2001.
  15. J. Shi and J. Malik, Normalized cuts and image segmentation. IEEE. Trans. on Pattern Analysis and Machine Intelligence, 2000.
  16. S. White and P. Smyth. A spectral clustering approach to finding communities in graphs. In SIAM, 2005.
  17. M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69, 2004.
  18. A. Clauset. Finding local community structure in networks. Physical Review E, 72:026132, 2005.
  19. S. Fortunato and M. Barthelemy. Resolution limit in community detection. PROC. NATL. ACAD. SCI. USA,104:36, 2007.
  20. J. Ruan and W. Zhang. Identifying network communities with a high resolution. Physical Review E, 77:016104, 2008.
  21. J. Scripps, P. -N. Tan, and A. -H. Esfahanian. , Exploration of link structure and community-based node roles in network. In ICDM, 2007.
  22. W. W. Zachary. An information ow model for conict and _ssion in small groups. Journal of Anthropological Research, 33:452-473, 1977.
  23. Jiyang Chen, Osmar R. Zaiane and Randy Geobel, Detecting Communities in Social Networks uaing Max-Min Modularity. SIAM, 2009.
  24. B. Yang, W. K. Cheung, and J. Liu, Community Mining from Signed Social Networks, IEEE Trans. Knowledge and Data Eng. , vol. 19, no. 10, pp. 1333-1348, Oct. 2007.
  25. M. E. J. Newman, Fast algorithm for detecting community structure in networks. Physical Review E, 69, 2004.
  26. M. Girvan and M. E. J. Newman, Community Structure in Social and Biological Networks, Proc. Nat'l Academy of Sciences USA, vol. 9, no. 12, pp. 7821-7826, 2002.
  27. M. E. J. Newman, Fast Algorithm for Detecting Community Structure in Networks, Physical Rev. E, vol. 69, no. 6, p. 066133, 2004.
  28. J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-904, 2000.
  29. M. Fiedler, Algebraic Connectivity of Graphs," Czechoslovakian Math. J. , vol. 23, pp. 298-305, 1973. "
  30. R. Guimera and L. A. N. Amaral, Functional Cartography of Complex Metabolic Networks, Nature, vol. 433, no. 2, pp. 895-900, 2005.
  31. D. Lusseau,. The Emergent Properties of a Dolphin Social Network, Proc. Royal Soc. B: Biological Sciences, vol. 270, no. Suppl 2, pp. S186-S188, 2003
  32. W. W. Zachary,"An Information Flow Model for Conflict and Fission in Small Groups, J. Anthropological Research, vol. 33, pp. 452-473, 1977.
  33. M. Girvan and M. E. J. Newman, "Community Structure in Social and Biological Networks," Proc. Nat'l Academy of Sciences USA, vol. 9, no. 12, pp. 7821-7826, 2002.
  34. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, Uncovering the Overlapping Community Structures of Complex Networks in Nature and Society, Nature, vol. 435, no. 7043, pp. 814-818, 2005.
  35. M. Girvan and M. E. J. Newman, Community structure in social and biological networks. In PNAS USA, 99:8271-8276, 2002.
  36. M. E. J. Newman, Fast algorithm for detecting community structure in networks. Physical Review E, 69, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Social networks Community Mining Hidden Communities Disconnected edges mining techniques

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