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Research Article
 

Multi-facet Community Detection from Bipartite Networks



Xu Yongcheng, Chen Ling and Zou Shengrong
 
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ABSTRACT

Detecting communities from networks is one of the important and challenging research topics in social network analysis, especially from bipartite network. In unipartite network, communities are usually represented as sets of nodes within which connections are dense but between which connections are sparse. However, communities in unipartite networks are not suitable to bipartite network, because there is only one-to-one correspondence between communities of different types. In this study we propose an algorithm for detecting communities from bipartite network based on ant colony optimization. Present algorithm allows many-to-many correspondence between communities in different parts. Experimental results demonstrate that tour algorithm can extract multi-facet communities from bipartite networks and obtain high quality of community partitioning.

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  How to cite this article:

Xu Yongcheng, Chen Ling and Zou Shengrong, 2013. Multi-facet Community Detection from Bipartite Networks. Journal of Applied Sciences, 13: 3137-3144.

DOI: 10.3923/jas.2013.3137.3144

URL: https://scialert.net/abstract/?doi=jas.2013.3137.3144
 

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