Detecting community structure in bipartite networks based on matrix factorisation

Abstract

Community detection in bipartite network is very important in the research on the theory and applications of complex network analysis. In this paper, an algorithm for detecting community structure in bipartite networks based on matrix factorisation is 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 the modularity cannot be further improved. While partitioning the network, we use 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 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.