Exchangeable random measures for sparse and modular graphs with overlapping communities

• 成果类型: Article
• 署名作者: Todeschini, Adrien; Miscouridou, Xenia; Caron, Francois
• 署名单位: Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux; University of Oxford
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12363
• 发表日期: 2020
• 页码: 487-520
• 关键词: Levy processes; models; distributions

摘要

We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop methods for efficient simulation of this class of random graphs and for scalable posterior inference. We show that the approach proposed can recover interpretable structure of real world networks and can handle graphs with thousands of nodes and tens of thousands of edges.