Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering

Abstract

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

23 pages, 9 figures

References in corpus (6)

• Fast unfolding of communities in large networks
• Cooperative Game Theory Approaches for Network Partitioning
• Statistical Mechanics of Community Detection
• Stochastic blockmodels and community structure in networks
• Phase transition in the detection of modules in sparse networks
• Community Detection as an Inference Problem