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Activity Number: 392 - Recent Advances in Tensor Learning
Type: Invited
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #316591
Title: Community Detection on Mixture Multi-Layer Networks via Regularized Tensor Decomposition
Author(s): Dong XIA*
Companies: Hong Kong University of Science and Technology
Keywords: Tensor decomposition; Multi-layer network; Community detection
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, ie, 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.


Authors who are presenting talks have a * after their name.

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