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)


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.


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.


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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

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