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SOMSN: An Effective Self Organizing Map for Clustering of Social Networks

by Fatemeh Ghaemmaghami, Reza Manouchehri Sarhadi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 5
Year of Publication: 2013
Authors: Fatemeh Ghaemmaghami, Reza Manouchehri Sarhadi
10.5120/14570-2692

Fatemeh Ghaemmaghami, Reza Manouchehri Sarhadi . SOMSN: An Effective Self Organizing Map for Clustering of Social Networks. International Journal of Computer Applications. 84, 5 ( December 2013), 7-12. DOI=10.5120/14570-2692

@article{ 10.5120/14570-2692,
author = { Fatemeh Ghaemmaghami, Reza Manouchehri Sarhadi },
title = { SOMSN: An Effective Self Organizing Map for Clustering of Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 5 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number5/14570-2692/ },
doi = { 10.5120/14570-2692 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:06.182733+05:30
%A Fatemeh Ghaemmaghami
%A Reza Manouchehri Sarhadi
%T SOMSN: An Effective Self Organizing Map for Clustering of Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 5
%P 7-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Graph Clustering is a fundamental problem in many areas of research. The purpose of clustering is to organize people, objects, and events in different clusters in such a way that there exist a relatively strong degree of association between the members of each cluster and a relatively weak degree of association between members of different clusters. In this paper, a new algorithm named self-organizing map for clustering social networks (SOMSN) is proposed for detecting such groups. SOMSN is based on self-organizing map neural network. In SOMSN, by adapting new weight-updating method, a social network is divided into different clusters according to the topological connection of each node. These clusters are the communities that mentioned above, in social networks. To show the effectiveness of the presented approach, SOMSN has been applied on several classic social networks with known number of communities and defined structure. The results of these experiments show that the clustering accuracy of SOMSN is superior compared to the traditional algorithms.

References
  1. M. Webster, "Cluster analysis," Merriam-Webster Online Dictionary, 2008. [Online]. Available: http://www. merriam-webster-online. com.
  2. B. Krishnamurthy and J. Wang, "On network-aware clustering of web clients," ACM SIGCOMM Computer Communication Review, vol. 30, no. 4, pp. 97–110, 2000.
  3. P. K. Reddy, M. Kitsuregawa, P. Sreekanth, and S. S. Rao, "A graph based approach to extract a neighborhood customer community for collaborative filtering," in Databases in Networked Information Systems, 2002, pp. 188–200.
  4. N. Jeyaratnarajah, "Cluster-Based Networks," in Ad hoc networking, 2001, pp. 75–138.
  5. B. Buter, N. Dijkshoorn, D. Modolo, Q. Nguyen, S. van Noort, B. van de Poel, A. Ali, and A. Salah, "Explorative visualization and analysis of a social network for arts: the case of deviantART," Journal of Convergence Volume, vol. 2, no. 1, 2011.
  6. M. Girvan and M. E. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, 2002.
  7. G. W. Flake, S. Lawrence, C. L. Giles, and F. M. Coetzee, "Self-organization and identification of web communities," Computer, vol. 35, no. 3, pp. 66–70, 2002.
  8. L. C. Freeman, "A set of measures of centrality based on betweenness," Sociometry, vol. 40, pp. 35–41, 1977.
  9. M. E. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, p. 026113, 2004.
  10. B. J. Frey and D. Dueck, "Clustering by passing messages between data points," science, vol. 315, no. 5814, pp. 972–976, 2007.
  11. S. Papadopoulos, A. Skusa, A. Vakali, Y. Kompatsiaris, and N. Wagner, "Bridge bounding: A local approach for efficient community discovery in complex networks," in arXiv preprint arXiv:0902. 0871, 2009.
  12. P. Zhao and C. -Q. Zhang, "A new clustering method and its application in social networks," Pattern Recognition Letters, vol. 32, no. 15, pp. 2109–2118, 2011.
  13. T. Kohonen, Self-organizing maps. Springer, 2001.
  14. T. Kohonen, "Self-organized formation of topologically correct feature maps," Biological cybernetics, vol. 43, no. 1, pp. 59–69, 1982.
  15. D. Roussinov and H. Chen, "A scalable self-organizing map algorithm for textual classification: A neural network approach to thesaurus generation," Communication Cognition and Artificial Intelligence, vol. 15, no. 1, pp. 81–111, 1998.
  16. S. Haykin, Neural networks: a comprehensive foundation. NewYork: Prentice Hall PTR, 1994.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Social Network Self-Organizing Map (SOM) Neural Networks

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