Latent Heterogeneous Multilayer Community Detection
Hafiz Tiomoko Ali, Sijia Liu, Yasin Yilmaz, Romain Couillet, Indika Rajapakse, Alfred O. Hero III
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.
BibTeX
@inproceedings{icassp2019_latentheterogene,
title = {Latent Heterogeneous Multilayer Community Detection},
author = {Hafiz Tiomoko Ali and Sijia Liu and Yasin Yilmaz and Romain Couillet and Indika Rajapakse and Alfred O. Hero III},
booktitle = {ICASSP 2019},
year = {2019}
}