Computer Science ›› 2014, Vol. 41 ›› Issue (2): 55-58.

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Detecting Community Structure in Bipartite Networks Based on Matrix Factorization

CHEN Bo-lun,CHEN Ling,ZOU Sheng-rong and XU Xiu-lian   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Community detection in bipartite network is very important in the reseach on the theory and applications of complex network analysis.An algorithm for detecting community structure in bipartite networks based on matrix factorization was presented.The algorithm first partitions the network into two parts,each of which can reserve the community information as much as possible,and then the two parts are further recursively partitioned until they can not be partitioned.When partitioning the network,we used the approach of matrix decomposition so that the row space of the associated matrix of the networks can be approximated as close as possible and the community information can be reserved the as much as possible.Experimental results show that our algorithm can not only accurately identify the number of communities of a network,but also obtain higher quality of community partitioning without previously known parameters.

Key words: Bipartite network,Matrix factorization,Detecting community structure

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