A Latent Space Model for Multilayer Network Data
This work addresses the challenge of analyzing interconnected social networks for researchers in network science and sociology, representing an incremental improvement with a novel hierarchical prior.
The authors tackled the problem of modeling multiple social networks on the same actors by proposing a Bayesian statistical model that balances dependency and independence, enabling tasks like visualization, consensus network generation, correlation measurement, and clustering across various real-world datasets.
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.