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An Improved Community Detection Method in Bipartite Networks

  • Fan Chunlong
  • Song Yan
  • Song Huimin
  • Ding GuohuiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Bipartite networks is one of the important research object in complex networks. At present, the bipartite networks community partition is mainly aimed at how to carry out accurate community structure, while the study in the community merge strategy is relative rare. In this paper, we study community partition merger principle in single nodes of bipartite networks to propose an improved bipartite networks community detection method, which is based on Page Rank algorithm, information spreading probability model and combined with the modularity. The information of single nodes is calculated by information diffusion matrix. The value of information diffusion matrix larger than the threshold are merged every time, which quickly reduces the dimensions of the information diffusion matrix to speed up the merger of the community significantly. By comparing and analyzing experimental result of this method with other typical bipartite networks community partition algorithm on South women data set, We demonstrate the effectiveness of the proposed method.

Keywords

Page Rank Bipartite networks Community division Information diffusion 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Fan Chunlong
    • 1
  • Song Yan
    • 1
  • Song Huimin
    • 1
  • Ding Guohui
    • 1
    Email author
  1. 1.Liaoning Provincial Key Lab of Large-scale Distributed SystemThe University of Shenyang AerospaceShenyanChina

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