Noise Tolerant Community Detection Using a Mixed Graph Model

  • Anita Keszler
  • Akos Kiss
  • Tamas Sziranyi
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 80)


In this paper a new concept is proposed for finding communities in a social network based on a mixed graph theoretic model of a standard and a bipartite graph. Compared to previous methods the introduced algorithm has the advantage of noise-tolerance and is applicable independently of the size of the clusters in the graph. The cluster core-mining method is based on a modified MST algorithm. Clustering incomplete data is done by using bipartite graphs and fuzzy membership functions.


Span Tree Bipartite Graph Social Network Analysis Community Detection Fuzzy Membership Function 
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 2010

Authors and Affiliations

  • Anita Keszler
    • 1
  • Akos Kiss
    • 1
  • Tamas Sziranyi
    • 1
  1. 1.Computer and Automation Research InstituteMTA SZTAKIBudapestHungary

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