CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model

TL;DR

This paper introduces CALF-SBM, a Bayesian network model that combines node covariates and latent factors for improved community detection, validated through simulations and real-world data applications.

Contribution

The paper presents CALF-SBM, a novel covariate-assisted latent factor stochastic block model with Bayesian inference for community detection in complex networks.

Findings

CALF-SBM outperforms classical clustering algorithms in simulations.

Effective estimation of the number of communities using model selection.

Successful application to medical collaboration and aviation networks.

Abstract

We propose a novel network generative model extended from the standard stochastic block model by concurrently utilizing observed node-level information and accounting for network-enabled nodal heterogeneity. The proposed model is so so-called covariate-assisted latent factor stochastic block model (CALF-SBM). The inference for the proposed model is done in a fully Bayesian framework. The primary application of CALF-SBM in the present research is focused on community detection, where a model-selection-based approach is employed to estimate the number of communities which is practically assumed unknown. To assess the performance of CALF-SBM, an extensive simulation study is carried out, including comparisons with multiple classical and modern network clustering algorithms. Lastly, the paper presents two real data applications, respectively based on an extremely new network data demonstrating collaborative relationships of otolaryngologists in the United States and a traditional aviation network data containing information about direct flights between airports in the United States and Canada.