Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition

• URL: http://arxiv.org/abs/2002.04457v1
• Date: Mon, 10 Feb 2020 06:19:50 GMT
• Title: Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
• Authors: Bing-Yi Jing and Ting Li and Zhongyuan Lyu and Dong Xia
• Abstract summary: We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers.
• Score: 12.244594819580831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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