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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2020
- DOI:
- 10.48550/arXiv.2002.04457
- arXiv:
- arXiv:2002.04457
- Bibcode:
- 2020arXiv200204457J
- Keywords:
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- Computer Science - Social and Information Networks;
- Computer Science - Information Theory;
- Computer Science - Machine Learning;
- Mathematics - Statistics Theory;
- Statistics - Methodology;
- Statistics - Machine Learning