Communities Unfolding in Multislice Networks

  • Vincenza Carchiolo
  • Alessandro Longheu
  • Michele Malgeri
  • Giuseppe Mangioni
Part of the Communications in Computer and Information Science book series (CCIS, volume 116)


Discovering communities in complex networks helps to understand the behaviour of the network. Some works in this promising research area exist, but communities uncovering in time-dependent and/or multiplex networks has not deeply investigated yet. In this paper, we propose a communities detection approach for multislice networks based on modularity optimization. We first present a method to reduce the network size that still preserves modularity. Then we introduce an algorithm that approximates modularity optimization (as usually adopted) for multislice networks, thus finding communities. The network size reduction allows us to maintain acceptable performances without affecting the effectiveness of the proposed approach.


Greedy Algorithm Community Detection Modularity Function Community Detection Algorithm Multiplex 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arenas, A., Duch, J., Fernandez, A., Gomez, S.: Size reduction of complex networks preserving modularity. New Journal of Physics 9, 176 (2007)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Asur, S., Parthasarathy, S., Ucar, D.: An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 913–921. ACM, New York (2007)Google Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (10), 10008 (2008)Google Scholar
  4. 4.
    Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On modularity clustering. IEEE Transactions on Knowledge and Data Engineering 20(2), 172–188 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 554–560. ACM, New York (2006)Google Scholar
  6. 6.
    Fenn, D.J., Porter, M.A., McDonald, M., Williams, S., Johnson, N.F., Jones, N.S.: Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007-2008 credit crisis. Chaos 19(3), 033119–+ (2009)CrossRefGoogle Scholar
  7. 7.
    Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Hopcroft, J., Khan, O., Kulis, B., Selman, B.: Tracking evolving communities in large linked networks. Proceedings of the National Academy of Sciences 101, 5249–5253 (April 2004)Google Scholar
  10. 10.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 611–617. ACM, New York (2006)Google Scholar
  11. 11.
    Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 016118 (2009)CrossRefGoogle Scholar
  12. 12.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar
  13. 13.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6(1), 29–123 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.-P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)CrossRefGoogle Scholar
  16. 16.
    Palla, G., Barabsi, A.L., Vicsek, T., Hungary, B.: Quantifying social group evolution. Nature 446 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vincenza Carchiolo
    • 1
  • Alessandro Longheu
    • 1
  • Michele Malgeri
    • 1
  • Giuseppe Mangioni
    • 1
  1. 1.Dipartimento di Ingegneria Elettrica, Elettronica e InformaticaUniversity of CataniaItaly

Personalised recommendations