A Generative Model for Exploring Structure Regularities in Attributed Networks

Abstract Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this paper, by characterizing the potential relationship between communities of links and node attributes, a principled statistical model named PSB_PG that generates link topology and node attributes is proposed. This model for generating links is based on the stochastic blockmodels following a Poisson distribution. Therefore, it is capable of detecting a wide range of network structures including community structures, bipartite structures, and other mixture structures. The model for generating node attributes assumes that node attributes are high-dimensional, sparse, and also follow a Poisson distribution. This makes the model be uniform, and the model parameters can be directly estimated by the expectation-maximization (EM) algorithm. Experimental results on artificial networks and real networks containing various structures have shown that the proposed model PSB_PG is not only competitive with the state-of-the-art models, but also provides a good semantic interpretation for each community via the learned relationship between the community and its related attributes.