Title: Latent heterogeneous multilayer community detection,
Abstract: We propose a method for simultaneously detecting shared and unshared
communities in heterogeneous multilayer weighted and undirected networks. The
multilayer network is assumed to follow a generative probabilistic model that
takes into account the similarities and dissimilarities between the
communities. We make use of a variational Bayes approach for jointly inferring
the shared and unshared hidden communities from multilayer network
observations. We show the robustness of our approach compared to state-of-the
art algorithms in detecting disparate (shared and private) communities on
synthetic data as well as on real genome-wide fibroblast proliferation dataset.