Community detection is of great importance for understanding graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communities in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropriate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.


Evidential modularity Evidential c-means Overlapping communities Credal partition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Costa, L.D.F., Oliveira Jr., O.N., Travieso, G., Rodrigues, F.A., Villas Boas, P.R., Antiqueira, L., Viana, M.P., Correa Rocha, L.E.: Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Advances in Physics 60(3), 329–412 (2011)CrossRefGoogle Scholar
  2. 2.
    Denœux, T., Masson, M.H.: Evclus: evidential clustering of proximity data. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(1), 95–109 (2004)CrossRefGoogle Scholar
  3. 3.
    Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Havens, T., Bezdek, J., Leckie, C., Ramamohanarao, K., Palaniswami, M.: A soft modularity function for detecting fuzzy communities in social networks. IEEE Transactions on Fuzzy Systems 21(6), 1170–1175 (2013)CrossRefGoogle Scholar
  6. 6.
    Havens, T.C., Bezdek, J.C., Leckie, C., Chan, J., Liu, W., Bailey, J., Ramamohanarao, K., Palaniswami, M.: Clustering and visualization of fuzzy communities in social networks. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–7. IEEE (2013)Google Scholar
  7. 7.
    Masson, M.H., Denoeux, T.: Ecm: An evidential version of the fuzzy c-means algorithm. Pattern Recognition 41(4), 1384–1397 (2008)zbMATHCrossRefGoogle Scholar
  8. 8.
    Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F.: Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E 77(1), 016107 (2008)Google Scholar
  9. 9.
    Newman, M.E.: Fast algorithm for detecting community structure in networks. Physical review E 69(6), 066133 (2004)Google Scholar
  10. 10.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 026113 (2004)Google Scholar
  11. 11.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)CrossRefGoogle Scholar
  12. 12.
    Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66(2), 191–234 (1994)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Smyth, S., White, S.: A spectral clustering approach to finding communities in graphs. In: Proceedings of the 5th SIAM International Conference on Data Mining, pp. 76–84 (2005)Google Scholar
  14. 14.
    Verma, D., Meila, M.: A comparison of spectral clustering algorithms. Tech. rep., UW CSE (2003)Google Scholar
  15. 15.
    Wang, X., Jiao, L., Wu, J.: Adjusting from disjoint to overlapping community detection of complex networks. Physica A: Statistical Mechanics and its Applications 388(24), 5045–5056 (2009)CrossRefGoogle Scholar
  16. 16.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473 (1977)Google Scholar
  17. 17.
    Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications 374(1), 483–490 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kuang Zhou
    • 1
    • 2
  • Arnaud Martin
    • 2
  • Quan Pan
    • 1
  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anP.R. China
  2. 2.IRISA, University of Rennes 1LannionFrance

Personalised recommendations