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 judgmentsthat 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.