A Novel Community Detection Algorithm for Privacy Preservation in Social Networks

  • Fatemeh Amiri
  • Nasser Yazdani
  • Heshaam Faili
  • Alireza Rezvanian
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


Developed online social networks are recently being grown and popularized tremendously, influencing some life aspects of human. Therefore, privacy preservation is considered as an essential and crucial issue in sharing and propagation of information. There are several methods for privacy preservation in social networks such as limiting the information through community detection. Despite several algorithms proposed so far to detect the communities, numerous researches are still on the way in this area. In this paper, a novel method for community detection with the assumption of privacy preservation is proposed. In the proposed approach is like hierarchical clustering, nodes are divided alliteratively based on learning automata (LA). A set of LA can find min-cut of a graph as two communities for each iteration. Simulation results on standard datasets of social network have revealed a relative improvement in comparison with alternative methods.


Social Networks Privacy Preservation Community Detection Top-Down Hierarchical Clustering Learning Automata 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fatemeh Amiri
    • 1
  • Nasser Yazdani
    • 1
  • Heshaam Faili
    • 1
  • Alireza Rezvanian
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of TehranTehranIran
  2. 2.Computer & IT Engineering DepartmentAmirkabir University of TechnologyTehranIran

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