Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels

• 成果类型: Article; Early Access
• 署名作者: Agterberg, Joshua; Lubberts, Zachary; Arroyo, Jesus
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia; Texas A&M University System; Texas A&M University College Station
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2516201
• 发表日期: 2025
• 关键词: consistent community detection; networks; selection

摘要

Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 - September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.