A Comparison of Methods for Community Detection in Large Scale Networks

  • Vinícius da Fonseca Vieira
  • Alexandre Gonçalves Evsukoff
Part of the Studies in Computational Intelligence book series (SCI, volume 424)


The modeling of complex systems by networks is an interesting approach for revealing the way that relationships occur and an increasing effort has been spent in the study of community structures. The main goal of this work is to show a comparative study of some of the state-of-art methods for community detection in large scale networks using modularity maximization. In this sense, we take into account not just the quality of the provided partitioning, but the computational cost associated to the method. Hence, we consider many aspects related to the algorithms efficiency, in order to provide the suitability to real scale applications. The results presented in this work are obtained from the literature, in a preliminar sense, and form a solid basis for the implementation and application of efficient algorithms for community detection in large scale networks.


Execution Time Community Detection Large Scale Network Modularity Variation Spectral Approach 
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 2013

Authors and Affiliations

  • Vinícius da Fonseca Vieira
    • 1
    • 2
  • Alexandre Gonçalves Evsukoff
    • 1
  1. 1.COPPE/UFRJ - Federal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.UFSJ - Federal University of São João del ReiSão João del ReiBrasil

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