Behavior-Based Twitter Overlapping Community Detection

  • Lixiang GuoEmail author
  • Zhaoyun Ding
  • Hui Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)


In this paper, we try to cluster twitter users into different communities. These communities can be overlapping based on their interests. The paper proposed a RWC (relation-weight-clustering) model to construct twitter users’ network. This model takes twitter users’ “@” and “RT@” behaviors into account. By counting their “@” and “RT@” frequency, the relation strength can be then descripted. Using SVM, we can get the users interest vector by analyzing their tweets. And the common interest vector between two users is calculated according to their common interests. Using community detection algorithm to resolve the relation-nodes-based network, the overlapping communities are formed with modularity of 0.682.


Overlapping community detection Interest space RWC model 


  1. 1.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E: Stat. Nonlin. Soft Matter Phys. 69(2Pt2), 026113–026113 (2004)CrossRefGoogle Scholar
  2. 2.
    Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010). doi: 10.1038/nature09182 CrossRefGoogle Scholar
  3. 3.
    Zhou, X.P., Liang, X.: User community detection on micro-blog using R-C model. J. Softw. 25(12), 2808–2823 (2014)Google Scholar
  4. 4.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 30(2), 155–168 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of Information Systems and ManagementNational University of Defense TechnologyChangshaChina

Personalised recommendations