A Comparison of Methods for Community Detection in Large Scale Networks

  • Vinícius da Fonseca Vieira
  • Alexandre Gonçalves Evsukoff
Part of the Studies in Computational Intelligence book series (SCI, volume 424)

Abstract

The modeling of complex systems by networks is an interesting approach for revealing the way that relationships occur and an increasing effort has been spent in the study of community structures. The main goal of this work is to show a comparative study of some of the state-of-art methods for community detection in large scale networks using modularity maximization. In this sense, we take into account not just the quality of the provided partitioning, but the computational cost associated to the method. Hence, we consider many aspects related to the algorithms efficiency, in order to provide the suitability to real scale applications. The results presented in this work are obtained from the literature, in a preliminar sense, and form a solid basis for the implementation and application of efficient algorithms for community detection in large scale networks.

Keywords

Execution Time Community Detection Large Scale Network Modularity Variation Spectral Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Facebook reaches 800 million users, http://migre.me/7vEqr (last access in January 12, 2012)
  2. 2.
    Facebook statistics by country, http://www.socialbakers.com/facebook-statistics/ (last access in January 12, 2012)
  3. 3.
    New study: Deep brand engagement correlates with financial performance, http://www.altimetergroup.com/2009/07/engagementdb.html (last access in January 12, 2012)
  4. 4.
    Arjomandi, E., Corneil, D.G.: Parallel computations in graph theory. In: 16th Annual Symposium on Foundations of Comp. Science, pp. 13–18 (October 1975), doi:10.1109/SFCS.1975.24Google Scholar
  5. 5.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (October 2008)Google Scholar
  6. 6.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70 (2004)Google Scholar
  7. 7.
    Danon, L., Diaz-Guilera, A., Arenas, A.: Effect of size heterogeneity on community identification in complex networks. Journal of Stat. Mech.: Theory and Experiment 6 (November 2006)Google Scholar
  8. 8.
    Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Heath, M.T.: Scientific Computing: An Introductory Survey, 2nd edn. McGraw-Hill (2002)Google Scholar
  10. 10.
    Leon-Suematsu, Y.I., Yuta, K.: Framework for fast identification of community structures in large-scale social networks. In: Data Mining for Social Network Data, Annals of Information Systems, vol. 12, pp. 149–175. Springer, US (2010)CrossRefGoogle Scholar
  11. 11.
    Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. Proc. of the National Academy of Sciences of the USA 101, (suppl. 1), 5200–5205 (2004)Google Scholar
  12. 12.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69(2), 1–5 (2004)Google Scholar
  13. 13.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America 103(23), 8577–8582 (2006)Google Scholar
  14. 14.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)Google Scholar
  15. 15.
    Newman, M.E.J.: Networks: An Introduction, 1st edn. Oxford University Press, USA (2010)MATHGoogle Scholar
  16. 16.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Review. E, Stat., Nonlinear and Soft Matter Physics 69(2) (2004)Google Scholar
  17. 17.
    Quiles, M.G., Zhao, L., Alonso, R.L., Romero, R.A.F.: Particle competition for complex network community detection. Chaos Woodbury NY 18(3), 107 (2008)MathSciNetGoogle Scholar
  18. 18.
    Quinn, M.J., Deo, N.: Parallel graph algorithms. ACM Comput. Surv. 16, 319–348 (1984), doi: http://doi.acm.org/10.1145/2514.2515Google Scholar
  19. 19.
    Sloan, J.D.: High Performance Linux Clusters with OSCAR, Rocks, OpenMosix, and MPI. O’Reilly (2004)Google Scholar
  20. 20.
    Tavakoli, F.: Parallel sparse matrix-vector multiplication (1997)Google Scholar
  21. 21.
    Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks. Analysis 105(2), 9 (2007)Google Scholar
  22. 22.
    Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., Demmel, J.: Optimization of sparse matrix-vector multiplication on emerging multicore platforms. Parallel Computing 35(3), 178–194 (2009)CrossRefGoogle Scholar
  23. 23.
    Zhang, Y., Wang, J., Wang, Y., Zhou, L.: Parallel community detection on large networks with propinquity dynamics. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining, p. 997 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vinícius da Fonseca Vieira
    • 1
    • 2
  • Alexandre Gonçalves Evsukoff
    • 1
  1. 1.COPPE/UFRJ - Federal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.UFSJ - Federal University of São João del ReiSão João del ReiBrasil

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