Advertisement

NPCCPM: An Improved Approach Towards Community Detection in Online Social Networks

  • Hilal Ahmad KhandayEmail author
  • Rana Hashmy
  • Aaquib Hussain Ganai
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 899)

Abstract

In this paper, we focus on the task community detection in social networks as it is the key aspect of complex network analysis. Lot of work has already been carried out on community detection, though most of the work done in this field is on non-overlapping communities. But in real networks, some nodes may belong to more than one community, so overlapping community detection needs more attention. The most popular technique for detecting overlapping communities is the Clique Percolation Method (CPM) which is based on the concept that the internal edges of a community are likely to form cliques due to their high density. CPM uses the term k-clique to indicate a complete sub-graph with k vertices. But it is not clear a priori which value of k one has to choose to detect the meaningful structures. Here we propose a method NO PARAMETER CORE CPM (NPCCPM) which calculates the value of k dynamically. Dynamic calculation of k makes it sure to give out the good community structure. We have developed a tool that improves the quality of simple CPM by making CPM-cover much more efficient by absorbing all the eligible nodes to communities and leaving out the bad nodes as outliers with respect to the given new detected cover.

Keywords

Social networks Community detection Clique CPM  NPCCPM Overlapping communities 

References

  1. 1.
    Fortunato, S.: Community detection in graphs. Physics reports (2010)Google Scholar
  2. 2.
    Schaeffer, S.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)CrossRefGoogle Scholar
  3. 3.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks, Natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: ICDM 2012 (2012)Google Scholar
  5. 5.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. (1973)Google Scholar
  6. 6.
    Girvan, M., Newman, M.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Newman, M.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  8. 8.
    Ahn, Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multi-scale complexity in networks. Nature 466(7307), 761 (2010)CrossRefGoogle Scholar
  9. 9.
    Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. JMLR 9, 1981–2014 (2007)zbMATHGoogle Scholar
  10. 10.
    Palla, G., Der´enyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814 (2005)CrossRefGoogle Scholar
  11. 11.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 76(3), 036106 (2007)Google Scholar
  12. 12.
    Subelj, S., Bajec M.: Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 83(3), 036103 (2011)Google Scholar
  13. 13.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  14. 14.
    Shang, R.H., et al.: Community detection based on modularity and an improved genetic algorithm. Phys. a-Stat. Mech. Appl. 392(5), 1215–1231 (2013)CrossRefGoogle Scholar
  15. 15.
    Blondel, V.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  16. 16.
    Shen, H.W., Cheng, X.Q.: Spectral methods for the detection of network community structure: a comparative analysis. J. Stat. Mech: Theory Exp. 2010(10), P10020 (2010)CrossRefGoogle Scholar
  17. 17.
    Jiang, J.Q., Dress, A.W.M., Yang, G.K.: A spectral clustering-based framework for detecting community structures in complex networks. Appl. Math. Lett. 22(9), 1479–1482 (2009)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Hilal Ahmad Khanday
    • 1
    Email author
  • Rana Hashmy
    • 1
  • Aaquib Hussain Ganai
    • 1
  1. 1.Department of Computer SciencesUniversity of KashmirSrinagarIndia

Personalised recommendations