A Latent Space Model for Multilayer Network Data

TL;DR

This paper introduces a Bayesian latent space model for analyzing multiple social networks over the same actors, enabling visualization, correlation measurement, and clustering of multilayer network data.

Contribution

It presents a hierarchical prior-based Bayesian model that jointly characterizes multiple networks, facilitating visualization, consensus network generation, and social analysis.

Findings

Effective visualization of multilayer networks in low-dimensional space

Ability to generate consensus weighted networks

Quantitative measures of network correlation and actor clustering

Abstract

In this work, we propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification easily allows us to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgements that subjects form about the relationships among actors, and perform clustering tasks at different social instances. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.