A Latent Space Model for Multilayer Network Data

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.

References in corpus (10)

• Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
• A Widely Applicable Bayesian Information Criterion
• Modeling homophily and stochastic equivalence in symmetric relational data
• Nonparametric Bayes dynamic modeling of relational data
• Latent Space Models for Dynamic Networks with Weighted Edges
• Latent Space Modeling of Multidimensional Networks with Application to the Exchange of Votes in Eurovision Song Contest
• Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
• Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
• Stochastic blockmodels for exchangeable collections of networks
• A Review of Latent Space Models for Social Networks