A General Approach for Modules Identification in Evolving Networks

  • Thang N. Dinh
  • Incheol Shin
  • Nhi K. Thai
  • My T. Thai
  • Taieb Znati
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)


Most complex networks exhibit a network modular property that is nodes within a network module are more densely connected among each other than with the rest of the network. Identifying network modules can help deeply understand the structures and functions of a network as well as design a robust system with minimum costs. Although there are several algorithms identifying the modules in literature, none can adaptively update modules in evolving networks without recomputing the modules from scratch. In this chapter, we introduce a general approach to efficiently detect and trace the evolution of modules in an evolving network. Our solution can identify the modules of each network snapshot based on the modules of previous snapshots, thus dynamically updating these modules. Moreover, we also provide a network compact representation which significantly reduces the size of the network, thereby minimizing the running time of any existing algorithm on the modules identification.


Network Module Community Detection Modular Structure Evolve Network 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.


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  1. 1.
    Macqueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967) Google Scholar
  2. 2.
    Donetti, L., Munoz, M.A.: Detecting network communities: a new systematic and efficient algorithm (October 2004) Google Scholar
  3. 3.
    Newman, M.E.J.: Modularity and partition in networks. In: Proceedings of the National Academy of Sciences, pp. 8577–8582 (2005) Google Scholar
  4. 4.
    White, S., Smyth, P.: A spectral partition approach to finding communities in graph. In: SIAM Data Mining Conference (2005) Google Scholar
  5. 5.
    Wu, F.: The Potts model. Rev. Mod. Phys. 54(1), 235 (1982) CrossRefGoogle Scholar
  6. 6.
    Reichardt, J., Bornholdt, S.: Detecting fuzzy partition in complex networks. Phys. Rev. Lett. 93(21) (2005) Google Scholar
  7. 7.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection (Mar 2006) Google Scholar
  8. 8.
    Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping partition of complex networks in nature and society. Nature 435, 814–818 (2005) CrossRefGoogle Scholar
  9. 9.
    Duch, J., Arenas, A.: Weighted network communities. New J. Phys. 9, 180 (2007) CrossRefMathSciNetGoogle Scholar
  10. 10.
    Palla, G., Farkas, I., Pollner, P., Derenyi, I., Vicsek, T.: Directed network communities. New J. Phys. 9, 186 (2007) CrossRefGoogle Scholar
  11. 11.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks (September 2003) Google Scholar
  12. 12.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks (August 2004) Google Scholar
  13. 13.
    Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72 (2005) Google Scholar
  14. 14.
    Guimera, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks (Aug 2004) Google Scholar
  15. 15.
    Hopcroft, J., Khan, O., Kulis, B., Selman, B.: Tracking evolving communities in large linked networks. Proc. Natl. Acad. Sci. USA 101(1), 5249–5253 (2004) CrossRefGoogle Scholar
  16. 16.
    Palla, G., Barabasi, A.: T. Vicsek. Quantifying social group evolution. Nature 446(7136), 664–667 (2007) CrossRefGoogle Scholar
  17. 17.
    Pollner, P., Palla, G., Vicsek, T.: Preferential attachment of communities: the same principle, but a higher level. Europhys. Lett. 73, 478 (2006) CrossRefMathSciNetGoogle Scholar
  18. 18.
    Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004) CrossRefGoogle Scholar
  19. 19.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(2) (2004) Google Scholar
  20. 20.
    Arenas, A., Duch, J., Fernandes, A., Gomez, S.: Size reduction of complex networks preserving modularity. New J. Phys. 9, 176–180 (2007) CrossRefGoogle Scholar
  21. 21.
    Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. P10008 (2008) Google Scholar
  22. 22.
    Arenas, A., Danon, L., Diaz-Guilera, A., Gleiser, P.M., Guimera, R.: Community analysis in social networks (2003) Google Scholar
  23. 23.
    Sun, J., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Graphscope: Parameter-free mining of large time-evolving graphs. In: KDD (August 2007) Google Scholar
  24. 24.
    Arxiv citation datasets. KDD Cup 2003, in Conjunction with the Ninth Annual ACM SIGKDD. (2003)

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Thang N. Dinh
    • 1
  • Incheol Shin
    • 1
  • Nhi K. Thai
    • 2
  • My T. Thai
    • 1
  • Taieb Znati
    • 3
  1. 1.University of FloridaGainesvilleUSA
  2. 2.University of MinnesotaMinneapolisUSA
  3. 3.University of PittsburghPittsburghUSA

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