Advertisement

Community Detection in Networks with Less Significant Community Structure

  • Ba-Dung Le
  • Hung Nguyen
  • Hong Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)

Abstract

Label propagation is a low complexity approach to community detection in complex networks. Research has extended the basic label propagation algorithm (LPA) in multiple directions including maximizing the modularity, a well-known quality function to evaluate the goodness of a community division, of the detected communities. Current state-of-the-art modularity-specialized label propagation algorithm (LPAm+) maximizes modularity using a two-stage iterative procedure: the first stage is to assign labels to nodes using label propagation, the second stage merges smaller communities to further improve modularity. LPAm+ has been shown able to achieve excellent performance on networks with significant community structure where the network modularity is above a certain threshold. However, we show in this paper that for networks with less significant community structure, LPAm+ tends to get trapped in local optimal solutions that are far from optimal. The main reason comes from the fact that the first stage of LPAm+ often misplaces node labels and severely hinders the merging operation in the second stage. We overcome the drawback of LPAm+ by correcting the node labels after the first stage. We apply a label propagation procedure inspired by the meta-heuristic Record-to-Record Travel algorithm that reassigns node labels to improve modularity before merging communities. Experimental results show that the proposed algorithm, named meta-LPAm+, outperforms LPAm+ in terms of modularity on networks with less significant community structure while retaining almost the same performance on networks with significant community structure.

Keywords

Community detection Label propagation LPAm meta-LPAm LPAm+ meta-LPAm+ 

Notes

Acknowledgments

The authors would like to thank the maintainers and contributors of the igraph packages used in this research.

References

  1. 1.
    Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80(2), 026129 (2009)CrossRefGoogle Scholar
  2. 2.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  3. 3.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)CrossRefGoogle Scholar
  4. 4.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)CrossRefGoogle Scholar
  5. 5.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal Complex Syst. 1695(5), 1–9 (2006)Google Scholar
  6. 6.
    Dueck, G.: New optimization heuristics: the great deluge algorithm and the record-to-record travel. J. Comput. Phys. 104(1), 86–92 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gleiser, P.M., Danon, L.: Community structure in jazz. Adv. Complex Syst. 6(4), 565–573 (2003)CrossRefGoogle Scholar
  10. 10.
    Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)CrossRefGoogle Scholar
  11. 11.
    Guimera, R., Amaral, L.A.N.: Cartography of complex networks: modules and universal roles. J. Stat. Mech. Theory Exp. 2005(2), P02001 (2005)CrossRefGoogle Scholar
  12. 12.
    Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)CrossRefGoogle Scholar
  13. 13.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabsi, A.L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)CrossRefGoogle Scholar
  14. 14.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Krebs, V.: A network of co-purchased books about us politics sold by the online bookseller amazon.com (2008). http://www.orgnet.com/
  16. 16.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar
  17. 17.
    Leung, I.X., Hui, P., Lio, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79(6), 066107 (2009)CrossRefGoogle Scholar
  18. 18.
    Liu, X., Murata, T.: Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Physica A Stat. Mech. Appl. 389(7), 1493–1500 (2010)CrossRefGoogle Scholar
  19. 19.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)CrossRefGoogle Scholar
  20. 20.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl Acad. Sci. 98(2), 404–409 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  22. 22.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  23. 23.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  24. 24.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Newman, M.E., Girvan, M.: Mixing patterns and community structure in networks. In: Pastor-Satorras, R., Rubi, M., Diaz-Guilera, A. (eds.) Statistical Mechanics of Complex Networks, pp. 66–87. Springer, Heidelberg (2003)Google Scholar
  26. 26.
    Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. AMS 56(9), 1082–1097 (2009)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar
  28. 28.
    Rosvall, M., Axelsson, D., Bergstrom, C.T.: The map equation. Eur. Phys. J. Spec. Top. 178(1), 13–23 (2009)CrossRefGoogle Scholar
  29. 29.
    Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys. Rev. E 77(4), 046112 (2008)CrossRefGoogle Scholar
  30. 30.
    Šubelj, L., Bajec, M.: Robust network community detection using balanced propagation. Eur. Phys. J. B Condens. Matter Complex Syst. 81(3), 353–362 (2011)CrossRefGoogle Scholar
  31. 31.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar
  32. 32.
    Zanetti, M.S., Schweitzer, F.: A network perspective on software modularity. In: ARCS Workshops (ARCS) 2012, pp. 1–8. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of AdelaideAdelaideAustralia

Personalised recommendations