A robust Bayesian latent position approach for community detection in networks with continuous attributes

TL;DR

This paper introduces a Bayesian mixture model for community detection in multiplex networks, jointly modeling node attributes and latent positions, demonstrating improved robustness and performance over benchmarks.

Contribution

The paper presents a novel Bayesian latent position model that accounts for dependencies across network layers and node attributes, enhancing community detection accuracy.

Findings

Outperforms existing benchmark models in simulations

Shows greater robustness with missing data

Successfully applied to real-world multi-layer network data

Abstract

The increasing prevalence of multiplex networks has spurred a critical need to take into account potential dependencies across different layers, especially when the goal is community detection, which is a fundamental learning task in network analysis. We propose a full Bayesian mixture model for community detection in both single-layer and multi-layer networks. A key feature of our model is the joint modeling of the nodal attributes that often come with the network data as a spatial process over the latent space. In addition, our model for multi-layer networks allows layers to have different strengths of dependency in the unique latent position structure and assumes that the probability of a relation between two actors (in a layer) depends on the distances between their latent positions (multiplied by a layer-specific factor) and the difference between their nodal attributes. Under our prior specifications, the actors' positions in the latent space arise from a finite mixture of Gaussian distributions, each corresponding to a cluster. Simulated examples show that our model outperforms existing benchmark models and exhibits significantly greater robustness when handling datasets with missing values. The model is also applied to a real-world three-layer network of employees in a law firm.