A Joint Community Detection Model: Integrating Directed and Undirected Probabilistic Graphical Models via Factor Graph With Attention Mechanism

Abstract

Directed and undirected probabilistic graphical models have been successfully used in community detection in recent years, but existing graphical model based methods usually only use one type of probabilistic graphical model to discover communities. However, directed and undirected graphical models have their own advantages for characterizing different network information (attribute information and network topology). Intuitively, we can make use of the merit of both kinds of models by combining them into a unified model. However, combining directed and undirected graphical models is difficult, as they have different properties which prevent parameter sharing and joint training. In this article, we propose a unified model which integrates directed and undirected graphical models by transforming both them into factor graph. In addition, as network topology and attribute information may contain different degrees of noises, we add a selective attention layer to learn the reliable weight of each type of information source in node granularity. For training the model, we derive an iterative belief propagation algorithm to train all the parameters simultaneously. Extensive experiments on real networks and artificial benchmarks show the superiority of our approach over existing methods.