Title: Diverse and Sparse Mixture-of-Experts for Causal Subgraph–Based Out-of-Distribution Graph Learning
Primary area: learning on graphs and other geometries & topologies
Keywords: graph neural network, out-of-distribution learning, mixture-of-experts

Abstract: Current state-of-the-art methods for out-of-distribution (OOD) generalization lack the ability to effectively address datasets with heterogeneous causal structures at the instance level. Existing approaches that attempt to handle such heterogeneity either rely on data augmentation, which risks altering label semantics, or impose causal assumptions whose validity in real-world datasets is uncertain. We introduce a novel Mixture-of-Experts (MoE) framework that can model heterogeneous causal structures without relying on restrictive assumptions. Our key idea is to address instance-level heterogeneity by enforcing semantic diversity among experts, each generating a distinct causal subgraph, while a learned gate assigns sparse weights that adaptively focus on the most relevant experts for each input. Our theoretical analysis shows that these two properties jointly reduce OOD error. In practice, our experts are scalable and do not require environment labels. Empirically, our framework achieves strong performance on the GOOD benchmark across both synthetic and real-world structural shifts.

TL;DR: We introduce a Mixture-of-Experts framework with diverse experts and sparse gating for graph OOD generalization, with a derived risk bound linking these properties to reduced OOD error.