Towards Democratic Group Detection in Complex Networks

  • Michele Coscia
  • Fosca Giannotti
  • Dino Pedreschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7227)

Abstract

To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple local-first approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.

Keywords

Crime Prevention Label Propagation Real World Network Community Quality Quantitative Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahn, Y.-Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)CrossRefGoogle Scholar
  2. 2.
    Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. SAM 4(5), 512–546 (2011)MathSciNetGoogle Scholar
  3. 3.
    Derényi, I., Palla, G., Vicsek, T.: Clique Percolation in Random Networks. Physical Review Letters 94(16), 160202 (2005)CrossRefGoogle Scholar
  4. 4.
    Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Sciences 104(1), 36–41 (2007)CrossRefGoogle Scholar
  5. 5.
    Goyal, A., On, B.-W., Bonchi, F., Lakshmanan, L.V.S.: Gurumine: A pattern mining system for discovering leaders and tribes. In: International Conference on Data Engineering, pp. 1471–1474 (2009)Google Scholar
  6. 6.
    Henderson, K., Eliassi-Rad, T., Papadimitriou, S., Faloutsos, C.: Hcdf: A hybrid community discovery framework. In: SDM, pp. 754–765 (2010)Google Scholar
  7. 7.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  8. 8.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Physical Review E (2007)Google Scholar
  9. 9.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  10. 10.
    Shen, H.-W., Cheng, X.-Q., Guo, J.-F.: Quantifying and identifying the overlapping community structure in networks. J. Stat. Mech. (2009)Google Scholar
  11. 11.
    Yonas, M.A., Borrebach, J.D., Burke, J.G., Brown, S.T., Philp, K.D., Burke, D.S., Grefenstette, J.J.: Dynamic Simulation of Community Crime and Crime-Reporting Behavior. In: Salerno, J., Yang, S.J., Nau, D., Chai, S.-K. (eds.) SBP 2011. LNCS, vol. 6589, pp. 97–104. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michele Coscia
    • 1
  • Fosca Giannotti
    • 2
  • Dino Pedreschi
    • 1
  1. 1.Computer Science Dep.University of PisaItaly
  2. 2.ISTI - CNR, Area della Ricerca di PisaItaly

Personalised recommendations