Latent heterogeneous multilayer community detection
Social and Information Networks
2019-06-25 v2 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1806.07963,
title = {Latent heterogeneous multilayer community detection},
author = {Hafiz Tiomoko Ali and Sijia Liu and Yasin Yilmaz and Romain Couillet and Indika Rajapakse and Alfred Hero},
journal= {arXiv preprint arXiv:1806.07963},
year = {2019}
}