Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
Abstract
We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.
Cite
@article{arxiv.2002.04457,
title = {Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition},
author = {Bing-Yi Jing and Ting Li and Zhongyuan Lyu and Dong Xia},
journal= {arXiv preprint arXiv:2002.04457},
year = {2020}
}