COMMUNITY DETECTION ON MIXTURE MULTILAYER NETWORKS VIA REGULARIZED TENSOR DECOMPOSITION

We study the problem of community detection in multilayer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, that is, mixture multilayer 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 number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multilayer 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.

Automated summary

The study introduces a framework for community detection in multilayer networks, using a tensor-based algorithm to accurately reveal global/local memberships of nodes and layers. Numerical studies confirm the effectiveness of the method, which is applied to real datasets producing new and interesting findings.