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Community detection is an important content in complex network analysis. Theexisting community detection methods in attributed networks mostly focus ononly using network structure, while the methods of integrating node attributesis mainly for the traditional community structures, and cannot detectmultipartite structures and mixture structures in network. In addition, themodel-based community detection methods currently proposed for attributednetworks do not fully consider unique topology information of nodes, such asbetweenness centrality and clustering coefficient. Therefore, a stochasticblock model that integrates betweenness centrality and clustering coefficientof nodes for community detection in attributed networks, named BCSBM, isproposed in this paper. Different from other generative models for attributednetworks, the generation process of links and attributes in BCSBM model followsthe Poisson distribution, and the probability between community is consideredbased on the stochastic block model. Moreover, the betweenness centrality andclustering coefficient of nodes are introduced into the process of links andattributes generation. Finally, the expectation maximization algorithm isemployed to estimate the parameters of the BCSBM model, and the node-communitymemberships is obtained through the hard division process, so the communitydetection is completed. By experimenting on six real-work networks containingdifferent network structures, and comparing with the community detectionresults of five algorithms, the experimental results show that the BCSBM modelnot only inherits the advantages of the stochastic block model and can detectvarious network structures, but also has good data fitting ability due tointroducing the betweenness centrality and clustering coefficient of nodes.Overall, the performance of this model is superior to other five comparedalgorithms.

A stochastic block model for community detection in attributed networks