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Partially Exchangeable Stochastic Block Models for (Node-Colored) Multilayer Networks

delete2025-07-22
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PRE
AI
D
Daniele Durante
F
Francesco Gaffi *
A
Antonio Lijoi
I
Igor Pruenster
DOI:10.1080/01621459.2025.2507825delete
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摘要

摘要

En 中文
Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single notion of connectivity is measured between nodes belonging to different pre-specified layers (node-colored networks). While progress has been made in statistical modeling of edge-colored networks, principled approaches that flexibly account for both within and across layer block-connectivity structures while incorporating layer information through a rigorous probabilistic construction are still lacking for node-colored multilayer networks. We fill this gap by introducing a novel class of partially exchangeable stochastic block models specified in terms of a hierarchical random partition prior for the allocation of nodes to groups, whose number is learned by the model. This goal is achieved without jeopardizing probabilistic coherence, uncertainty quantification and derivation of closed-form predictive within- and across-layer co-clustering probabilities. Our approach facilitates prior elicitation, the understanding of theoretical properties and the development of yet-unexplored predictive strategies for both the connections and the allocations of future incoming nodes. Posterior inference proceeds via a tractable collapsed Gibbs sampler, while performance is illustrated in simulations and in a real-world criminal network application. The notable gains achieved over competitors clarify the importance of developing general stochastic block models based on suitable node-exchangeability structures coherent with the type of multilayer network being analyzed. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keyword:
Bayesian nonparametrics
Hierarchical normalized completely random measure
Node-colored network
Partial exchangeability
Partially exchangeable partition probability function

期刊

J
Journal of the American Statistical Association
IF:
3
论文数:
5.2K
被引数:
4.8W

机构

B
Bocconi University
学者数:
1.9K
论文数: 2.4K
被引数: 4.8K
C
College Park
学者数:
818
论文数: 416
被引数: 5
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