  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.
