Identifying Hierarchical Structures in Network Data

TL;DR

This paper proposes a hierarchical stochastic blockmodel extension to detect multilevel community structures in networks, providing algorithms for inference and demonstrating effectiveness on both simulated and real data.

Contribution

It introduces a novel hierarchical stochastic blockmodel and inference algorithms, advancing community detection by identifying multilevel structures in network data.

Findings

The model accurately detects communities and supercommunities in datasets.

MCMC outperforms variational Bayes in accuracy, especially for smaller networks.

The model defaults to a single supercommunity when no multilevel structure exists.

Abstract

In this paper, we introduce a hierarchical extension of the stochastic blockmodel to identify multilevel community structures in networks. We also present a Markov chain Monte Carlo (MCMC) and a variational Bayes algorithm to fit the model and obtain approximate posterior inference. Through simulated and real datasets, we demonstrate that the model successfully identifies communities and supercommunities when they exist in the data. Additionally, we observe that the model returns a single supercommunity when there is no evidence of multilevel community structure. As expected in the case of the single-level stochastic blockmodel, we observe that the MCMC algorithm consistently outperforms its variational Bayes counterpart. Therefore, we recommend using MCMC whenever the network size allows for computational feasibility.