Community Detection for Multilayer Heterogeneous Network
Abstract
Many real world networks consist of multiple types of nodes with edges that are heterogeneous in nature. However, most of the existing work for community detection only focused on homogeneous network consisting of a single layer. In this paper, we propose a modified Degree-Corrected Stochastic Model (DCBM) for modeling multilayer heterogeneous network. We develop a spectral clustering method that can unify the information contained in each sub-network, and demonstrate its efficiency to detect communities on simulated data and on Authorship/Citation network data. As a by-product, we present a novel algorithm called BiScore for clustering bipartite network under DCBM, and show that under mild conditions BiScore is guaranteed to yield consistent results.
Cite
@article{arxiv.1705.05967,
title = {Community Detection for Multilayer Heterogeneous Network},
author = {Fan Yang and Fengshuo Zhang},
journal= {arXiv preprint arXiv:1705.05967},
year = {2017}
}
Comments
We were not aware of the fact that similar ideas have already appeared in the literature. We decide to withdraw the manuscript and apologize for any confusion