B0725
Title: A latent space model for multilayer network data
Authors: Brenda Betancourt - NORC at the University of Chicago (United States) [presenting]
Abstract: A Bayesian statistical model is proposed to simultaneously characterize two or more social networks defined over a common set of actors. The model's key feature is a hierarchical prior distribution that allows the user to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among other 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.
