Latent heterogeneous multilayer community detection

TL;DR

This paper introduces a probabilistic variational Bayes method for detecting shared and unshared communities in multilayer networks, outperforming existing algorithms on synthetic and real biological data.

Contribution

It presents a novel probabilistic model and inference approach for joint detection of shared and private communities in heterogeneous multilayer networks.

Findings

Outperforms state-of-the-art algorithms on synthetic data

Effective in real genome-wide fibroblast proliferation data

Accurately detects both shared and unshared communities

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 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.