Title: 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 potential relationship between
link communities 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 and sparse and also follow a Poisson
distribution. This makes the model be uniform and the model parameters can be
directly estimated by 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 good semantic interpretation for
each community via the learned relationship between the community and its
related attributes.