Uncovering Overlap Community Structure in Complex Networks Using Particle Competition

  • Fabricio Breve
  • Liang Zhao
  • Marcos Quiles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5855)


Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new clustering method to uncover overlap nodes in complex networks is proposed. It is based on particles walking and competing with each other, using random-deterministic movement. The new community detection algorithm can output not only hard labels, but also continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations were performed with synthetic and real data and good results were achieved.


complex networks community detection overlap community structure particle competition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  2. 2.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kauffman, San Francisco (2005)zbMATHGoogle Scholar
  3. 3.
    Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
  4. 4.
    Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  5. 5.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson/Addison Wesley (2005)Google Scholar
  6. 6.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Dorogovtsev, S., Mendes, F.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford (2003)zbMATHGoogle Scholar
  8. 8.
    Bornholdt, S., Schuster, H.: Handbook of Graphs and Networks: From the Genome to the Internet. Wiley-VCH (2006)Google Scholar
  9. 9.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (1–15) (2004)Google Scholar
  10. 10.
    Newman, M.: Modularity and community structure in networks. Proceedings of the National Academy of Science of the United States of America 103, 8577–8582 (2006)CrossRefGoogle Scholar
  11. 11.
    Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Physical Review E 72, 027104 (1–4) (2006)Google Scholar
  12. 12.
    Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a potts model. Physical Review Letters 93, 218701 (1–4) (2004)Google Scholar
  13. 13.
    Danon, L., Díaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 9, P09008 (1–10) (2005)Google Scholar
  14. 14.
    Quiles, M.G., Zhao, L., Alonso, R.L., Romero, R.A.F.: Particle competition for complex network community detection. Chaos 18, 033107 (1–10) (2008)Google Scholar
  15. 15.
    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, 483–490 (2007)CrossRefGoogle Scholar
  16. 16.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  17. 17.
    Zhang, S., Wang, R.S., Zhang, X.S.: Uncovering fuzzy community structure in complex networks. Physical Review E 76, 046103 (1–7) (2007)Google Scholar
  18. 18.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fabricio Breve
    • 1
  • Liang Zhao
    • 1
  • Marcos Quiles
    • 1
  1. 1.Institute of Mathematics and Computer ScienceUniversity of São PauloSão CarlosBrazil

Personalised recommendations