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Reseach Article

Community Kernels Detection in OSN using SVM Clustering and Classification

by Rahul Nema, Anjana Pandey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 11
Year of Publication: 2015
Authors: Rahul Nema, Anjana Pandey

Rahul Nema, Anjana Pandey . Community Kernels Detection in OSN using SVM Clustering and Classification. International Journal of Computer Applications. 113, 11 ( March 2015), 9-13. DOI=10.5120/19869-1854

@article{ 10.5120/19869-1854,
author = { Rahul Nema, Anjana Pandey },
title = { Community Kernels Detection in OSN using SVM Clustering and Classification },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 11 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { },
doi = { 10.5120/19869-1854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:50:39.527331+05:30
%A Rahul Nema
%A Anjana Pandey
%T Community Kernels Detection in OSN using SVM Clustering and Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 11
%P 9-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Security is an important issue in online social networking web sites. Here in OSN users can post their messages publicly on wall. In OSN a person may be attached to a community and can post any message on their friend's wall, hence it is necessary to check the validity of the user in the communities. Although there are various techniques implemented for the detection of community kernels in OSN. Here in this paper a new and efficient technique for the detection of community kernels in large OSN using combinatorial method of support vector machine based clustering and classification of Community kernels in the dataset is proposed. The proposed technique implemented provides high precision and recall as compared to the existing technique of Greedy and WEBA.

  1. Bruhn, J. : The Sociology of Community Connections. Springer Science+Business Media B. V. , 2011
  2. Girvan, M. and Newman, M. E. J. : Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821-7826, 2002.
  3. Wellman, B. : The network community: An introduction to networks in the global village. Networks in the Global Village, 1999
  4. S. D. Berkowitz. An Introduction to Structural Analysis: The Network Approach to Social Research. Toronto: Butterworths, 1982.
  5. Lada A. Adamic. The small world web. In Proceedings of the third European Conference on Research and Advanced Technology for Digital Libraries, ECDL, number 1696, pages 443–452. Springer-Verlag, 1999.
  6. Réka Albert and Albert-László Barabási. Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47–97, 2002.
  7. Faust K. Centrality in af?liation networks. Social Networks, 19:157–191, April 1997.
  8. M. S. Granovetter. The strength of weak ties. American Journal of Sociology, 78:1360–1380, 1973.
  9. BarryWellman and ScotWortley. Different strokes from different folks: Community ties and social support. The American Journal of Sociology, 96(3):558–588, November 1990.
  10. Newman, M. "Modularity and community structure in networks". Proceedings of the National Academy of Sciences of the United States of America 103(23):8577—82, 2006.
  11. Fortunato, S. "Community detection in graphs" Physics Reports 486(3–5):75-174, Physics Reports, 2010.
  12. Yangping Zhao, Jizhuang Zhao, and Rongsheng Xu. Network information content security: a framework for intelligent analysis and monitoring. icsssm, 2:841–843 Vol. 2, 2005.
  13. Zhen Zhang; Xiao-Ming Wang; Yun-Xiao Wang. A p2p global trust model based on recommendation. Machine Learning and Cybernetics, Proceedings of 2005 International Conference on, 7:3975–3980 Vol. 7, 18-21 Aug. 2005.
  14. P. Oscar Boykin and Vwani P. Roychowdhury. Leveraging social networks to ?ght spam. Computer, 38(4):61–68, 2005.
  15. Robert D. Nolker and Lina Zhou. Social computing and weighting to identify member roles in online communities. In Web Intelligence, pages 87–93, 2005.
  16. Nasrullah Memon and Henrik Legind Larsen. Practical algorithms for destabilizing terrorist networks. In Proceedings of the The First International Conference on Availability, Reliability and Security, ARES, pages 389–400, 2006.
  17. Rong Qian, Wei Zhang, and Bingru Yang. Detect community structure from the enron email corpus based on link mining. In Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), pages 850–855, IEEE Computer Society, 2006
Index Terms

Computer Science
Information Sciences


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