Advertisement

Detecting Communities in Massive Networks Efficiently with Flexible Resolution

  • Qi YeEmail author
  • Bin Wu
  • Bai Wang
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)

Abstract

Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this chapter, we propose an iterative heuristic algorithm (called MMO algorithm) to extract the community structure in large networks based on local multi-resolution modularity optimization whose time complexity is near linear and space complexity is linear. The effectiveness of MMO algorithm is demonstrated by extensive experiments on lots of computer generated graphs and publically available real-world graphs. We also extend MMO algorithm to extract communities in distributed environment and use it to explore a massive call graph on a normal PC. The results show that MMO algorithm is very efficient, and it may enhance our ability to explore massive networks in real time.

Keywords

Jaccard Index Call Graph Community Detection Algorithm Modularity Optimization Patent Citation Network 
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.

Notes

Acknowledgements

We thank M. E. J. Newman, Alex Arenas and Jure Leskovec for providing us the network data sets. This work is supported by the National Science Foundation of China (No. 90924029, 60905025, 61074128). It is also supported the National Hightech R&D Program of China (No.2009AA04Z136).

References

  1. 1.
    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466, 761–764 (2010)CrossRefGoogle Scholar
  2. 2.
    Blondel, V.D., Guillaume, J., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. 10008, 1-Ű12 (2008)Google Scholar
  3. 3.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  4. 4.
    Cormen, T.H., Leiserson, C.E., et al.: Introduction to Algorithms, 2nd edn. MIT, Cambridge (2001)zbMATHGoogle Scholar
  5. 5.
    Danon, L., Duch, J., et al.: Comparing community structure identification. J. Stat. Mech. 9008, 09008 (2005)CrossRefGoogle Scholar
  6. 6.
    Dongen, S.V.: Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht (2000)Google Scholar
  7. 7.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104, 36–41 (2007)CrossRefGoogle Scholar
  9. 9.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Hu, Y., Chen, H., et al.: Comparative definition of community and corresponding identifying algorithm. Phys. Rev. E 78, 026121 (2008)CrossRefGoogle Scholar
  11. 11.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78, 046110 (2008)CrossRefGoogle Scholar
  12. 12.
    Leung, I.X.Y., Hui, P., et al.: Towards real-time community detection in large networks. Phys. Rev. E 79, 066107 (2009)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)CrossRefGoogle Scholar
  14. 14.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)CrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  16. 16.
    Radicchi, F., Castellano, C., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. 101, 2658–2663 (2004)CrossRefGoogle Scholar
  17. 17.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)CrossRefGoogle Scholar
  18. 18.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. 100, 12123–12128 (2003)CrossRefGoogle Scholar
  20. 20.
    Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques. In: COCOON, pp. 161–170. Springer, Berlin (2004)Google Scholar
  21. 21.
    Ye, Q., Wu, B., et al: TeleComVis: exploring temporal communities in telecom networks. In: ECML PKDD, pp. 755–758. Springer, Heidelberg (2009)Google Scholar
  22. 22.
    Ye, Q., Wu, B., et al: Detecting communities in massive networks based on local community attractive force optimization. In: International Conference on Advances in Social Network Analysis and Mining, pp. 291–295. IEEE Computer Society, Los Alamitos (2010)Google Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations