A multi-level generative framework for community detection in attributed networks

Community detection in attributed networks is one of the most important tasks in complex network analysis. Many existing methods propose to integrate the network topology and node attribute from a generative aspect, which models an attributed network as a probabilistic generation process with the community distribution described by hidden variables. Though they can provide good interpretability to the community structure, it is difficult to infer community membership quickly due to their high computational complexity when inferring. Motivated by the multi-level strategy, in this study, we propose a multi-level generative framework to reduce the time cost of generative models for community detection in attributed networks. We first coarsen an attributed network into smaller ones by node matching. Then, we employ the existing generative model on the coarsest network without any modification for community detection, thus efficiently obtaining community memberships of nodes in this small coarsest network. Last, we project the assignments back to the original network through a local refinement mechanism to get communities. Extensive experiments on several real-world and artificial attributed networks show that our multi-level-based method is significantly faster than original generative models and is able to achieve better or more competitive results.