ECODE: Event-Based Community Detection from Social Networks

  • Xiao-Li Li
  • Aloysius Tan
  • Philip S. Yu
  • See-Kiong Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6587)


People regularly attend various social events to interact with other community members. For example, researchers attend conferences to present their work and to network with other researchers. In this paper, we propose an E vent-based COmmunity DEtection algorithm ECODE to mine the underlying community substructures of social networks from event information. Unlike conventional approaches, ECODE makes use of content similarity-based virtual links which are found to be more useful for community detection than the physical links. By performing partial computation between an event and its candidate relevant set instead of computing pair-wise similarities between all the events, ECODE is able to achieve significant computational speedup. Extensive experimental results and comparisons with other existing methods showed that our ECODE algorithm is both efficient and effective in detecting communities from social networks.


social network mining community detection virtual links 


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  1. 1.
    Albert, R., Jeong, H., Barabási, A.-L.: Diameter of the world-wide web. Nature 401, 130–131 (1999)CrossRefGoogle Scholar
  2. 2.
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  3. 3.
    Li, X.-L., et al.: Searching for Rising Stars in Bibliography Networks. In: DASFAA (2009)Google Scholar
  4. 4.
    Palla, G., et al.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  5. 5.
    Li, X.-L., et al.: Interaction Graph Mining for Protein Complexes Using Local Clique Merging. Genome Informatics 16(2) (2005)Google Scholar
  6. 6.
    Li, X.-L., Foo, C.-S., Ng, S.-K.: Discovering Protein Complexes in Dense Reliable Neighborhoods of Protein Interaction Networks. In: CSB (2007)Google Scholar
  7. 7.
    Steinhaeuser, K., Chawla, N.: A Network-Based Approach to Understanding and Predicting Diseases. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Wu, M., et al.: A Core-Attachment based Method to Detect Protein Complexes in PPI Networks. BMC Bioinformatics 10(169) (2009)Google Scholar
  9. 9.
    Li, X.-L., et al.: Computational approaches for detecting protein complexes from protein interaction networks: a survey. BMC Genomics 11(Suppl 1:S3) (2010)Google Scholar
  10. 10.
    Redner, S.: How popular is your paper? An Empirical Study of the Citation Distribution. Eur. Phys. J. B(4), 131–138 (1998)Google Scholar
  11. 11.
    Nisheeth, S., Anirban, M., Rastogi, R.: Mining (Social) Network Graphs to Detect Random Link Attacks. In: ICDE (2008)Google Scholar
  12. 12.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  13. 13.
    Radicchi, F., et al.: Defining and identifying communities in networks. PNAS 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  14. 14.
    Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  16. 16.
    Ravasz, E., et al.: Hierarchical Organization of Modularity in Metabolic Networks. Science 297, 1551–1555 (2002)CrossRefGoogle Scholar
  17. 17.
    Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72 (2005)Google Scholar
  18. 18.
    Boccaletti, S., et al.: Detection of Complex Networks Modularity by Dynamical Clustering. Physical Review E, 75 (2007)Google Scholar
  19. 19.
    Shen, H., et al.: Detect overlapping and hierarchical community structure in networks. CoRR abs/0810.3093 (2008)Google Scholar
  20. 20.
    Seidman, S.B.: Network structure and minimum degree. Social Networks 5, 269–287 (1983)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Holme, P., Huss, M., Jeong, H.: Subnetwork hierarchies of biochemical pathways. Bioinformatics 19(4), 532–538 (2003)CrossRefGoogle Scholar
  22. 22.
    Gleiser, P., Danon, L.: Community structure in jazz. Advances in Complex Systems 6, 565 (2003)CrossRefGoogle Scholar
  23. 23.
    Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as Spectroscopy: Automated Discovery of Community Structure within Organizations. Communities and Technologies, 81–96 (2003)Google Scholar
  24. 24.
    Gregory, S.: A fast algorithm to find overlapping communities in networks. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 408–423. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Bie, T.D., Cristianini, N.: Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problems. Journal of Machine Learning Research 7, 1409–1436 (2006)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Newman, M.E.J.: Detecting community structure in networks. European Physical Journal B 38, 321–330 (2004)CrossRefGoogle Scholar
  28. 28.
    Ding, C., He, X., Zha, H.: A Spectral Method to Separate Disconnected and Nearly-disconnected Web Graph Components. In: KDD (2001)Google Scholar
  29. 29.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  30. 30.
    Backstrom, L., et al.: Group Formation in Large Social Networks: Membership, Growth, and Evolution. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, USA (2006)Google Scholar
  31. 31.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD (2005)Google Scholar
  32. 32.
    Kimura, M., Saito, K.: Tractable Models for Information Diffusion in Social Networks. In: ECML/PKDD (2006)Google Scholar
  33. 33.
    Tang, L., et al.: Community Evolution in Dynamic Multi-Mode Networks. In: SIGKDD (2008)Google Scholar
  34. 34.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: SIGMOD Conference (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiao-Li Li
    • 1
  • Aloysius Tan
    • 1
  • Philip S. Yu
    • 2
  • See-Kiong Ng
    • 1
  1. 1.Institute for Infocomm ResearchSingapore
  2. 2.Department of Computer ScienceUniversity of IllinoisChicagoUSA

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