Community Detection for Clustered Attributed Graphs via a Variational EM Algorithm

Abstract

Community detection for attributed graphs, also called attributed graph clustering, is a new challenging issue in data mining due to the increasing emergence of different kinds of real-word networks with rich attributes. The existing works for the attributed graph clustering can be divided into two classes, namely distanced-based approaches and model-based approaches. In this paper, we focus on a model-based approach called clustered attributed graph model proposed by Xu et al. [12]. Instead of the original variational Bayes EM algorithm (VBEM) for solving this model, we propose a new variational EM algorithm (VEM). Comparing with the VBEM algorithm, our proposed VEM algorithm can reduce the number of parameters when fitting the model, which brings the lower computational complexity and easier implementation in practice. Additionally, a good model selection criterion ICL can be easily derived under the VEM framework. Our proposed VEM algorithm is demonstrated to perform competitively over the existing state of the art VBEM algorithm in terms of the extensive simulations and the real data.