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Identifying overlapping communities in social networks using multi-scale local information expansion

  • H. J. Li
  • J. Zhang
  • Z. P. Liu
  • L. ChenEmail author
  • X. S. ZhangEmail author
Regular Article

Abstract

Most existing approaches for community detection require complete information of the graph in a specific scale, which is impractical for many social networks. We propose a novel algorithm that does not embrace the universal approach but instead of trying to focus on local social ties and modeling multi-scales of social interactions occurring in those networks. Our method for the first time optimizes the topological entropy of a network and uncovers communities through a novel dynamic system converging to a local minimum by simply updating the membership vector with very low computational complexity. It naturally supports overlapping communities through associating each node with a membership vector which describes node’s involvement in each community. Furthermore, different multi-scale partitions can be obtained by tuning the characteristic size of modules from the optimal partition. Because of the high efficiency and accuracy of the algorithm, it is feasible to be used for the accurate detection of community structures in real networks.

Keywords

Statistical and Nonlinear Physics 

References

  1. 1.
    A.L. Barabási, R. Albert, Science 286, 509 (1999) MathSciNetCrossRefGoogle Scholar
  2. 2.
    R. Albert, A.L. Barabási, H. Jeong, Nature 401, 130 (1999) ADSCrossRefGoogle Scholar
  3. 3.
    X.G. Li, Z.Y. Gao, K.P. Li, X.M. Zhao, Phys. Rev. E 76, 016110 (2007) ADSCrossRefGoogle Scholar
  4. 4.
    F. Liljeros, C.R. Edling, L.A.N. Amaral, H.E. Stanley, Y. Aberg, Nature 411, 907 (2001) ADSCrossRefGoogle Scholar
  5. 5.
    A. Sumiyoshi, S. Norikazu, Phys. Rev. E 74, 026113 (2006) CrossRefGoogle Scholar
  6. 6.
    M.E.J. Newman, Phys. Rev. E 69, 066133 (2004) ADSCrossRefGoogle Scholar
  7. 7.
    M.E.J. Newman, M. Girvan, Phys. Rev. E 69, 026113 (2004) ADSCrossRefGoogle Scholar
  8. 8.
    M.E.J. Newman, Proc. Natl. Acad. Sci. 103, 8577 (2006) ADSCrossRefGoogle Scholar
  9. 9.
    L. Danon, J. Duch, D. Guilera, A. Arenas, J. Stat. Mech. 29, P09008 (2005) CrossRefGoogle Scholar
  10. 10.
    X.S. Zhang, R.S. Wang, Y. Wang, J. Wang, Y. Qiu, L. Wang, L. Chen, Europhys. Lett. 87, 38002 (2009) ADSCrossRefGoogle Scholar
  11. 11.
    A. Clauset, M.E.J. Newman, C. Moore, Phys. Rev. E 70, 066111 (2004) ADSCrossRefGoogle Scholar
  12. 12.
    M.E.J. Newman, Phys. Rev. E 74, 036104 (2006) MathSciNetADSCrossRefGoogle Scholar
  13. 13.
    Z.P. Li, S.H. Zhang, R.S. Wang, X.S. Zhang, L. Chen, Phys. Rev. E 77, 036109 (2007) ADSCrossRefGoogle Scholar
  14. 14.
    T. Evans, R. Lambiotte, Eur. Phys. J. B 77, 265 (2010)ADSCrossRefGoogle Scholar
  15. 15.
    P.J. Mucha, T. Richardson, K. Macon, M.A. Porter, J.P. Onnela, Science 328, 876 (2010) MathSciNetADSzbMATHCrossRefGoogle Scholar
  16. 16.
    G. Palla, I. Derényi, I. Farkas, T. Vicsek, Nature 435, 814 (2005) ADSCrossRefGoogle Scholar
  17. 17.
    H.J. Li, Y. Wang, L.Y. Wu, Z.P. Liu, L. Chen, X.S. Zhang, Europhys. Lett. 97, 48005 (2012) ADSCrossRefGoogle Scholar
  18. 18.
    J.S. Baras, P. Hovareshti, Proceedings of 47th IEEE Conference on Decision and Control (2008), pp. 2973–2978 Google Scholar
  19. 19.
    D. Gfeller, J.C. Chappelier, P. De Los Rios, Phys. Rev. E 72, 056135 (2005) ADSCrossRefGoogle Scholar
  20. 20.
    G. Bianconi, P. Pin, M. Marsili, Proc. Natl. Acad. Sci. 106, 11433 (2009) ADSCrossRefGoogle Scholar
  21. 21.
    E. Ravasz, A.L. Barabási, Phys. Rev. E 67, 026112 (2003) ADSCrossRefGoogle Scholar
  22. 22.
    D.B. Chen, M.S. Shang, Y. Fu, Physica A 389, 4177 (2010) ADSCrossRefGoogle Scholar
  23. 23.
    M.S. Shang, D.B. Chen, T. Zhou, Chin. Phys. Lett. 27, 058901 (2010) ADSCrossRefGoogle Scholar
  24. 24.
    A. Lancichinetti, S. Fortunato, Phys. Rev. E 80, 016118 (2009) ADSCrossRefGoogle Scholar
  25. 25.
    W.W. Zachary, J. Anthropol. Res. 33, 452 (1977)Google Scholar
  26. 26.
    M. Girvan, M.E.J. Newman, Proc. Natl. Acad. Sci. 99, 7821 (2002) MathSciNetADSzbMATHCrossRefGoogle Scholar
  27. 27.
    G. Palla, A.L. Barabási, T. Vicsek, Nature 446, 664 (2007) ADSCrossRefGoogle Scholar
  28. 28.
    V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, J. Stat. Mech. 10, P10008 (2005) Google Scholar
  29. 29.
    M. Rosvall, C.T. Bergstrom, Proc. Natl. Acad. Sci. 105, 1118 (2008) ADSCrossRefGoogle Scholar
  30. 30.
    A. Lancichinetti, S. Fortunato, Phys. Rev. E 80, 056117 (2009) ADSCrossRefGoogle Scholar
  31. 31.
    M.E.J. Newman, Eur. Phys. J. B 38, 321 (2004)ADSCrossRefGoogle Scholar
  32. 32.
    J. Zhang, S. Zhang, X.S. Zhang, Physica A 387, 1675 (2008) ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingP.R. China
  2. 2.National Center for Mathematics and Interdisciplinary SciencesChinese Academy of SciencesBeijingP.R. China
  3. 3.Key Laboratory of Random Complex Structures and Data ScienceChinese Academy of SciencesBeijingP.R. China
  4. 4.Key Laboratory of Systems Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiP.R. China
  5. 5.Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial ScienceUniversity of TokyoTokyoJapan

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