A latent space model for multilayer network data

Abstract

A Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors is proposed. The key feature of the model is a hierarchical prior distribution that allows the user to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification provides an easy way 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 judgments that subjects form about the relationships among actors, and perform clustering tasks at different social instances. The model's capabilities are illustrated using real-world and synthetic datasets, taking into account different types of actors, sizes, and relations.