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

Yaozu Cui and Xingyuan Wang

Physica A: Statistical Mechanics and its Applications, 2016, vol. 457, issue C, 307-315

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.

Keywords: Bipartite networks; Community structures; Bipartite clustering triangular; Weighted clustering threshold (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:457:y:2016:i:c:p:307-315

DOI: 10.1016/j.physa.2016.03.002

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