Community detection in multi-layer bipartite networks

TL;DR

This paper introduces a novel degree-corrected stochastic co-block model and an efficient spectral co-clustering algorithm tailored for community detection in multi-layer bipartite networks, demonstrating improved accuracy and practical effectiveness.

Contribution

The paper presents a new model and algorithm specifically designed for multi-layer bipartite networks, addressing limitations of existing methods and proving their theoretical and practical advantages.

Findings

Increased layers improve community detection accuracy.

The proposed algorithm outperforms existing methods in experiments.

Application to real datasets yields meaningful insights.

Abstract

The problem of community detection in multi-layer undirected networks has received considerable attention in recent years. However, practical scenarios often involve multi-layer bipartite networks, where each layer consists of two distinct types of nodes. Existing community detection algorithms tailored for multi-layer undirected networks are not directly applicable to multi-layer bipartite networks. To address this challenge, this paper introduces a novel multi-layer degree-corrected stochastic co-block model specifically designed to capture the underlying community structure within multi-layer bipartite networks. Within this framework, we propose an efficient debiased spectral co-clustering algorithm for detecting nodes' communities. We establish the consistent estimation property of our proposed algorithm and demonstrate that an increased number of layers in bipartite networks improves the accuracy of community detection. Through extensive numerical experiments, we showcase the superior performance of our algorithm compared to existing methods. Additionally, we validate our algorithm by applying it to real-world multi-layer network datasets, yielding meaningful and insightful results.