A Latent Position Co-Clustering Model for Multiplex Networks

TL;DR

This paper introduces LaPCoM, a Bayesian nonparametric latent co-clustering model for multiplex networks that jointly identifies network and node clusters, enabling detailed pattern discovery at multiple levels.

Contribution

It proposes a novel hierarchical mixture-of-mixtures model for simultaneous network and node clustering within a unified latent space framework.

Findings

Accurately recovers latent network and node clusters in simulations.

Effectively identifies meaningful clusters in real-world social multiplex data.

Provides interpretable results aligned with social roles and patterns.

Abstract

Multiplex networks are increasingly common across diverse domains, motivating the development of clustering methods that uncover patterns at multiple levels. Existing approaches typically focus on clustering either entire networks or nodes within a single network. We address the lack of a unified latent space framework for simultaneous network- and node-level clustering by proposing a latent position co-clustering model (LaPCoM), based on a hierarchical mixture-of-mixtures formulation. LaPCoM enables co-clustering of networks and their constituent nodes, providing joint dimension reduction and two-level cluster detection. At the network level, it identifies global homogeneity in topological patterns by grouping networks that share similar latent representations. At the node level, it captures local connectivity and community patterns. The model adopts a Bayesian nonparametric framework using a mixture of finite mixtures, which places priors on the number of clusters at both levels and incorporates sparse priors to encourage parsimonious clustering. Inference is performed via Markov chain Monte Carlo with automatic selection of the number of clusters. LaPCoM accommodates both binary and count-valued multiplex data. Simulation studies and comparisons with existing methods demonstrate accurate recovery of latent structure and clusters. Applications to real-world social multiplexes reveal interpretable network-level clusters aligned with context-specific patterns, and node-level clusters reflecting social patterns and roles.