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International Conference on Parallel Problem Solving from Nature

PPSN 2012: Parallel Problem Solving from Nature - PPSN XII pp 327–336Cite as

A Memetic Algorithm for Community Detection in Complex Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7492))

Abstract

Community detection is an important issue in the field of complex networks. Modularity is the most popular partition-based measure for community detection of networks represented as graphs. We present a hybrid algorithm mixing a dedicated crossover operator and a multi-level local optimization procedure. Experimental evaluations on a set of 11 well-known benchmark graphs show that the proposed algorithm attains easily all the current best solutions and even improves 6 of them in terms of maximum modularity.

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References

  1. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp., P10008 (October 2008), doi:10.1088/1742-5468/2008/10/P10008

    Google Scholar 

  2. Boguñá, M., Pastor-Satorras, R., Díaz-Guilera, A., Arenas, A.: Models of social networks based on social distance attachment. Phys. Rev. E 70(5), 056122 (2004)

    Article  Google Scholar 

  3. Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)

    Article  Google Scholar 

  4. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)

    Google Scholar 

  5. Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72(2), 027104 (2005)

    Article  Google Scholar 

  6. Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  7. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99(12), 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gleiser, P., Danon, L.: Community structure in social and biological networks. Advances in Complex Systems 6, 565–573 (2003)

    Article  Google Scholar 

  9. Grossman, J.: The Erdös number project (2007), http://www.oakland.edu/enp/

  10. Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)

    Article  Google Scholar 

  11. KDD. Cornell kdd cup (2003), http://www.cs.cornell.edu/projects/kddcup/

  12. Krebs, V.: A network of books about recent us politics sold by the online bookseller amazon.com. (2008), http://www.orgnet.com

  13. Liu, X., Murata, T.: Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Phys. A 389(7), 1493–1500 (2009)

    Google Scholar 

  14. Lü, Z., Huang, W.: Iterated tabu search for identifying community structure in complex networks. Phys. Rev. E 80(2), 026130 (2009)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Neri, F., Cotta, C., Moscato, P. (eds.): Handbook of Memetic Algorithms. SCI, vol. 379. Springer (2011)

    Google Scholar 

  17. Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2), 404–409 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  18. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  19. Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)

    Google Scholar 

  20. Noack, A., Rotta, R.: Multi-level Algorithms for Modularity Clustering. In: Vahrenhold, J. (ed.) SEA 2009. LNCS, vol. 5526, pp. 257–268. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  21. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Amer. Statistical Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  22. Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys. Rev. E 77(4), 046112 (2008)

    Article  Google Scholar 

  23. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIUM & IUT, Université du Maine, Av. O. Messiaen, 72085, Le Mans, France

    Olivier Gach

  2. LERIA, Université d’Angers, 2 Bd Lavoisier, 49045, Angers Cedex 01, France

    Olivier Gach & Jin-Kao Hao

Authors
  1. Olivier Gach
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  2. Jin-Kao Hao
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Editor information

Editors and Affiliations

  1. Departmento de Computation, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. IPN No. 2508, Col. San Pedro Zacatenco, 0360, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Vincenzo Cutello  & Mario Pavone  & 

  3. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology, Kanpur, Kanpur, India

    Kalyanmoy Deb

  4. Department of Computer Science, University of New Mexico, USA

    Stephanie Forrest

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia

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© 2012 Springer-Verlag Berlin Heidelberg

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Gach, O., Hao, JK. (2012). A Memetic Algorithm for Community Detection in Complex Networks. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_33

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  • DOI: https://doi.org/10.1007/978-3-642-32964-7_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32963-0

  • Online ISBN: 978-3-642-32964-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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