A Generative Model for Exploring Structure Regularities in Attributed Networks

TL;DR

This paper introduces PSB_PG, a statistical generative model that jointly captures network topology and node attributes, enabling the detection of diverse structural patterns and providing semantic insights into communities.

Contribution

The paper proposes a novel generative model combining link and attribute generation, capable of identifying various network structures and interpreting community semantics.

Findings

PSB_PG effectively detects community, bipartite, and mixed structures.

The model outperforms state-of-the-art methods on artificial and real networks.

It provides meaningful semantic interpretations of communities.

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.