Ants Crawling to Discover the Community Structure in Networks

  • Mariano Tepper
  • Guillermo Sapiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

We cast the problem of discovering the community structure in networks as the composition of community candidates, obtained from several community detection base algorithms, into a coherent structure. In turn, this composition can be cast into a maximum-weight clique problem, and we propose an ant colony optimization algorithm to solve it. Our results show that the proposed method is able to discover better community structures, according to several evaluation criteria, than the ones obtained with the base algorithms. It also outperforms, both in quality and in speed, the recently introduced FG-Tiling algorithm.

References

  1. 1.
    Aldecoa, R., Marín, I.: Jerarca: efficient analysis of complex networks using hierarchical clustering. PLoS ONE 5(7), e11585+ (2010)Google Scholar
  2. 2.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(026113) (2004)Google Scholar
  3. 3.
    Brendel, W., Todorovic, S.: Segmentation as maximum-weight independent set. In: NIPS (2010)Google Scholar
  4. 4.
    Ion, A., Carreira, J., Sminchisescu, C.: Image segmentation by figure-ground composition into maximal cliques. In: ICCV (2011)Google Scholar
  5. 5.
    Li, N., Latecki, L.J.: Clustering aggregation as maximum-weight independent set. In: NIPS (2012)Google Scholar
  6. 6.
    Leguizamon, G., Michalewicz, Z., Schutz, M.: An ant system for the maximum independent set problem. In: CACIC, vol. 2 (2001)Google Scholar
  7. 7.
    Solnon, C., Fenet, S.: A study of ACO capabilities for solving the maximum clique problem. J. Heuristics 12(3), 155–180 (2006)CrossRefMATHGoogle Scholar
  8. 8.
    Katayama, K., Hamamoto, A., Narihisa, H.: An effective local search for the maximum clique problem. Inf. Process. Lett. 95(5), 503–511 (2005)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Algorithmica 29(4), 610–637 (2001)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Aldecoa, R., Marín, I.: Deciphering network community structure by surprise. PLoS ONE 6(9), e24195+ (2011)Google Scholar
  11. 11.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 284–293 (2004)MathSciNetGoogle Scholar
  12. 12.
    Knuth, D.E.: The Stanford GraphBase: A Platform for Combinatorial Computing. ACM, New York (1993)Google Scholar
  13. 13.
    Baird, D., Ulanowicz, R.E.: The seasonal dynamics of the Chesapeake bay ecosystem. Ecol. Monogr. 59(4), 329–364 (1989)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Evans, T.S., Rivers, R.J., Knappett, C.: Interactions in space for archaeological models. Adv. Complex Syst. 15, 1150009+ (2011)Google Scholar
  16. 16.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: an empirical evaluation. In: CVPR (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mariano Tepper
    • 1
  • Guillermo Sapiro
    • 1
  1. 1.Department of Electrical and Computer EngineeringDuke UniversityUSA

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