CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Comparative analysis of community discovery methods in social networks

by Dr. M. Mohamed Sathik, Dr. K. Senthamarai Kannan, A. Abdul Rasheed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 8
Year of Publication: 2011
Authors: Dr. M. Mohamed Sathik, Dr. K. Senthamarai Kannan, A. Abdul Rasheed
10.5120/1902-2536

Dr. M. Mohamed Sathik, Dr. K. Senthamarai Kannan, A. Abdul Rasheed . Comparative analysis of community discovery methods in social networks. International Journal of Computer Applications. 14, 8 ( February 2011), 27-31. DOI=10.5120/1902-2536

@article{ 10.5120/1902-2536,
author = { Dr. M. Mohamed Sathik, Dr. K. Senthamarai Kannan, A. Abdul Rasheed },
title = { Comparative analysis of community discovery methods in social networks },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 14 },
number = { 8 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number8/1902-2536/ },
doi = { 10.5120/1902-2536 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:52.447380+05:30
%A Dr. M. Mohamed Sathik
%A Dr. K. Senthamarai Kannan
%A A. Abdul Rasheed
%T Comparative analysis of community discovery methods in social networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 8
%P 27-31
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The study of networks is an active area of research due to its capability of modeling many real world complex systems. Social Network gains its popularity due to its ease of use. It enables people all over the world to interact with each other with the advent of Web 2.0 in this Internet era. Online Social Networking facilitates people to have communication nevertheless of considering geographical location over the globe. Social Network Analysis is the field of research that provides a set of tools and theoretical approaches for holistic exploration of the communication and interaction patterns of social systems. A common pattern among the group of people in a network is considered as a community which is a partition of the entire network structure. There are few existing methods for discovering communities. We introduced a method called “mutual accessibility” for community discovery. This article compares such three different methods including the one that we introduced. The results of those methods are also shown by taking various datasets as an analysis.

References
  1. Shuzi Niu, Daling Wang, Shi Feng, Ge yu, 2009, An improved spectral clustering algorithm for community discovery, Ninth Intl. Conf. on Hybrid Intelligent Systmes, Vol. 3, 262-267.
  2. Andrea Lancichinetti, Santo Fortunato, 2009, Community detection algorithms: a comparative analysis, arXiv: 0908.1062vl physics soc-ph.
  3. Clara Pizzuti, 2008, Community detection in social networks with Genetic Algorithms, Proceedings of the 10th annual conference on genetic and evolutionary computation, 1137-1138.
  4. Daudin J. J , Pichard F and Robin S, 2008, A mixture model for random graphs, statistical computing 18, 173-183.
  5. Michael Chaua, Jennifer Xu, 2007, Mining communities and their relationships in blogs: a study of online hate groups, Int. J. human – computer studies 65, 57-70.
  6. Shihua Zhang, Rui-Sheng Wang, Xiang-Sun Zhang, 2007, Identification of overlapping community structure in complex networks using fuzzy c-means clustering, Physica A374, 483-490.
  7. Raghavan U N, Albert R and Kumara S, 2007, Near linear time algorithm to detect community structures in large – scale networks, Physical Review E76, 036106.
  8. Newman M E J, 2006, Finding community structure using the eigenvectors of matrices, Physical Review E74, 036104.
  9. Richardt J and Bornholdt S, 2006, Statistical mechanics of community detection, Physical Review E74, 016110.
  10. Pascal Pons, Matthieu Latapy, 2005, Computing communities in large networks using random walks, LNCS 3733, 284-293.
  11. Jordi Duch an dAlex Arenas, 2005, Community detection in complex networks using extremal optimization, Physical review E72, 027104.
  12. Newman M E J, 2004, Fast algorithm for detecting community structure in networks, Physical Review E69, 066133.
  13. Narasimhamurthy A, D. Greene, N. hurley and P. Cunningham, 2008, Community finding in large social networks through problem decomposition, 19th Irish conference on Artificial Intelligence and cognitive science (AICS’08).
  14. Dr. M. Mohamed Sathik, A. Abdul Rasheed, 2010, discovering communities in social networks through mutual accessibility, Intl. Jnl on computer science and engineering, vol. 02, no. 04, 1423-1428.
  15. Robert Tarjan, 1972, Depth – first search and linear graph algorithms, SIAM J. Computing, Vol. 1 , No.2, 146-160.
  16. Zachary W. W, 1977, An information flow model for conflict and fission in small groups, Journal of Anthropological Research, 33, 452-473.
  17. Girvan M, Newman M. E. J, 2002, Proc. Natl. Acad. Sci, USA 99, 7821-7826
  18. Gleiser P, Danon L, 2003, Adv. Complex Syst 6, 565
  19. Guimera R, Danon L, diaz-Guilera A, Giralf F, Arenas A, 2003, Self-similar community structure in a network of human interactions, Physical Review E, vol 68, 06503
  20. Opsahl T, Panzarasa P, 2009, Clustering in weighted networks, Social Networks 31 (2), 155-163.
  21. Narasimhamurthy A, Greene D, Hurley N, Cunningham P, 2008, Scaling community finding algorithms to work for large networks through problem decomposition, 19th Irish Conference on Artificial Intelligence and Cognitive Science (AICS’08), Cork, Ireland.
  22. Guardiola X, Guimera R, Arenas A, diaz-Guilera A, Streib D, Amaral L. A. N, 2002, Macro- and micro-structure of trust networks, arXiv: cond-mat/0206240v1
  23. Newman M. E. J, 2001, the structure of scientific collaboration networks, Proc. Natl. Acad. Sci., USA98, 404-409.
  24. Minas Gjoka, Macief Kurant, Carter T Butts, Athina Markopoulou, 2010, Walking in Facebook: A case study of unbiased sampling of OSNs, Proceedings of IEEE INFOCOMM ’10.
Index Terms

Computer Science
Information Sciences

Keywords

Graph Clustering Social Networks Social Network Analysis Community Discovery