Overlapping Community Detection with Two-Level Expansion by Local Clustering Coefficients

  • Yi-Jen Su
  • Che-Chun Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)


Community detection is crucial to Social Network Analysis (SNA) in that it helps to discover high-density overlapping communities hidden in complex networks for advanced applications. This study proposed a novel community detection method by seed set expansion. The method gathered meaningful nodes into a seed set, which was then used as a central node to merge neighbor nodes until communities were found. To enhance efficiency, a two-level expansion approach was further developed, which adopted the 80/20 rule and involved threshold change in order to discover cohesive subgroups of smaller sizes. To detect overlapping communities, local clustering coefficients (LCC) were calculated to measure the interaction density between neighbor nodes and determine whether they expanded or not. The experiment results were evaluated by measuring the cohesion quality of communities.


Social network analysis Community detection Clustering coefficients 


  1. 1.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  2. 2.
    Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94(16), 160202 (2005)CrossRefGoogle Scholar
  3. 3.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  4. 4.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)Google Scholar
  5. 5.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Flake, G., Lawrence, S., Lee Giles, C.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–160 (2000)Google Scholar
  7. 7.
    Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the Web for emerging cyber-communities. Comput. Netw. 31(11–16), 1481–1493 (1999)CrossRefGoogle Scholar
  8. 8.
    Dhillon, I., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors: a multilevel approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1944–1957 (2007)CrossRefGoogle Scholar
  9. 9.
    Whang, J.J., Gleich, D.F., Dhillon, I.S.: Overlapping community detection using seed set expansion. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2099–2108 (2013)Google Scholar
  10. 10.
    Havemann, F., Heinz, M., Struck, A., Glaser, J.: Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels. J. Stat. Mech. Theor. Exp. 2011, P01023 (2011)CrossRefGoogle Scholar
  11. 11.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1988)CrossRefzbMATHGoogle Scholar
  12. 12.
    Furlan, V.: Vilfredo Pareto, Manuale di Economia Politica. Jahrbücher für Nationalökonomie und Statistik 91(1), 826–831 (1908)CrossRefGoogle Scholar
  13. 13.
    Turner, J.C.: Towards a cognitive redefinition of thesocial group. Social identity and intergroup, pp. 15–40 (1982)Google Scholar
  14. 14.
    Whang, J., Gleich, D., Dhillon, I.: Overlapping community detection using neighborhood-inflated seed expansion. IEEE Trans. Knowl. Data Eng. 28(5), 1272–1284 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shu-Te UniversityKaohsiung CityTaiwan

Personalised recommendations