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Detecting one-mode communities in bipartite networks by bipartite clustering triangular

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  • Cui, Yaozu
  • Wang, Xingyuan

Abstract

In this paper, an algorithm is proposed to detect one-mode community structures in bipartite networks, and to deduce which one-mode community structures are weighted. After analyzing the topological properties in bipartite networks, bipartite clustering triangular is introduced. First, bipartite networks are projected into two weighted one-mode networks by bipartite clustering triangular. Then all the maximal sub-graphs from two one-mode weighted networks are extracted and the maximal sub-graphs are merged together using a weighted clustering threshold. In addition, the proposed algorithm successfully finds overlapping vertices between one-mode communities. Experimental results using some real-world network data shows that the performance of the proposed algorithm is satisfactory.

Suggested Citation

  • Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:307-315
    DOI: 10.1016/j.physa.2016.03.002
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    References listed on IDEAS

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