Abstract: Stochastic block models learn group structures between nodes sharing similar connectivity patterns. Recent developments have extended this approach to multiple connected networks, often within multilayer or multiplex architectures. However, most formulations still rely on the strong assumption that all networks share a single node partition. In many applications, this assumption is too restrictive. To address this, we introduce partially exchangeable enriched stochastic block models, a new class of Bayesian network models that jointly capture multiple layers of dependency through partially exchangeable priors on node partitions. Building on the partially exchangeable stochastic block model of Durante et al (2025), we extend its construction to an enriched framework where two partitions are linked by a nested structure.

Host: Prof. Deborah Sulem