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We propose a new algorithm to detect the community structure in a networkthat utilizes both the network structure and vertex attribute data. Suppose wehave the network structure together with the vertex attribute data, that is,the information assigned to each vertex associated with the community to whichit belongs. The problem addressed this paper is the detection of the communitystructure from the information of both the network structure and the vertexattribute data. Our approach is based on the Bayesian approach that models theposterior probability distribution of the community labels. The detection ofthe community structure in our method is achieved by using belief propagationand an EM algorithm. We numerically verified the performance of our methodusing computer-generated networks and real-world networks.

Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering