Clustering of heterogeneous populations of networks

TL;DR

This paper introduces a Bayesian mixture model framework for clustering heterogeneous network populations, accommodating variations in network structures across different conditions or groups, with efficient sampling methods demonstrated on real and synthetic data.

Contribution

It presents a novel Bayesian clustering approach for heterogeneous network data that accounts for multiple underlying structures, with a fast Gibbs sampling algorithm for inference.

Findings

Successfully clusters networks into meaningful groups

Demonstrates effectiveness on real-world social and brain network data

Provides a scalable inference method for complex network populations

Abstract

Statistical methods for reconstructing networks from repeated measurements typically assume that all measurements are generated from the same underlying network structure. This need not be the case, however. People's social networks might be different on weekdays and weekends, for instance. Brain networks may differ between healthy patients and those with dementia or other conditions. Here we describe a Bayesian analysis framework for such data that allows for the fact that network measurements may be reflective of multiple possible structures. We define a finite mixture model of the measurement process and derive a fast Gibbs sampling procedure that samples exactly from the full posterior distribution of model parameters. The end result is a clustering of the measured networks into groups with similar structure. We demonstrate the method on both real and synthetic network populations.