Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization

Understand

Graph out-of-distribution (OOD) generalization remains a major challenge in graph learning since graph neural networks (GNNs) often suffer from severe performance degradation under distribution shifts.

• Invariant learning, aiming to extract invariant features across varied distributions, has recently emerged as a promising approach for OOD generation.
• Despite the great success of invariant learning in OOD problems for Euclidean data (i.e., images), the exploration within graph data remains constrained by the complex nature of graphs.
• Existing studies, such as data augmentation or causal intervention, either suffer from disruptions to invariance during the graph manipulation process or face reliability issues due to a lack of supervised signals for causal parts.