Stochastic blockmodels for exchangeable collections of networks

TL;DR

This paper introduces a new class of Bayesian nonparametric stochastic blockmodels for jointly analyzing multiple networks, enabling community structure comparison and capturing realistic network properties with improved MCMC inference.

Contribution

It presents a novel stochastic blockmodel framework that jointly models multiple networks and compares their community structures, using Bayesian nonparametrics and advanced MCMC techniques.

Findings

Effective joint estimation of multiple network structures.

Ability to compare community structures across networks.

Demonstrated on simulated and real-world data.

Abstract

We construct a novel class of stochastic blockmodels using Bayesian nonparametric mixtures. These model allows us to jointly estimate the structure of multiple networks and explicitly compare the community structures underlying them, while allowing us to capture realistic properties of the underlying networks. Inference is carried out using MCMC algorithms that incorporates sequentially allocated split-merge steps to improve mixing. The models are illustrated using a simulation study and a variety of real-life examples.