Stochastic block model and exploratory analysis in signed networks

TL;DR

This paper introduces a generalized stochastic block model for signed networks that uncovers hidden mesoscopic structures, identifies key vertices, and reveals overlapping communities without prior assumptions.

Contribution

It presents a novel stochastic block model tailored for signed networks, enabling structural pattern extraction and vertex role identification through model fitting.

Findings

Effective in detecting network structures in synthetic data

Successfully applied to real-world signed networks

Outperforms existing models in structure recognition

Abstract

We propose a generalized stochastic block model to explore the mesoscopic structures in signed networks by grouping vertices that exhibit similar positive and negative connection profiles into the same cluster. In this model, the group memberships are viewed as hidden or unobserved quantities, and the connection patterns between groups are explicitly characterized by two block matrices, one for positive links and the other for negative links. By fitting the model to the observed network, we can not only extract various structural patterns existing in the network without prior knowledge, but also recognize what specific structures we obtained. Furthermore, the model parameters provide vital clues about the probabilities that each vertex belongs to different groups and the centrality of each vertex in its corresponding group. This information sheds light on the discovery of the networks' overlapping structures and the identification of two types of important vertices, which serve as the cores of each group and the bridges between different groups, respectively. Experiments on a series of synthetic and real-life networks show the effectiveness as well as the superiority of our model.