Latent heterogeneous multilayer community detection
This addresses community detection in complex networks, particularly for biological data like genome-wide fibroblast proliferation, but appears incremental as it builds on existing probabilistic models.
The authors tackled the problem of detecting both shared and unshared communities in heterogeneous multilayer networks, and their method outperformed state-of-the-art algorithms on synthetic and real genomic data.
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 that our approach outperforms 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.