Discovering overlapping communities in multi-layer directed networks (2407.16152v1)

Abstract: This article explores the challenging problem of detecting overlapping communities in multi-layer directed networks. Our goal is to understand the underlying asymmetric overlapping community structure by analyzing the mixed memberships of nodes. We introduce a new model, the multi-layer mixed membership stochastic co-block model (multi-layer MM-ScBM), to model multi-layer directed networks in which nodes can belong to multiple communities. We develop a spectral procedure to estimate nodes' memberships in both sending and receiving patterns. Our method uses a successive projection algorithm on a few leading eigenvectors of two debiased aggregation matrices. To our knowledge, this is the first work to detect asymmetric overlapping communities in multi-layer directed networks. We demonstrate the consistent estimation properties of our method by providing per-node error rates under the multi-layer MM-ScBM framework. Our theoretical analysis reveals that increasing the overall sparsity, the number of nodes, or the number of layers can improve the accuracy of overlapping community detection. Extensive numerical experiments are conducted to validate these theoretical findings. We also apply our method to one real-world multi-layer directed network, gaining insightful results.