  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. In this work, we propose a novel framework, called Invariant
Graph Learning based on Information bottleneck theory (InfoIGL), to extract the
invariant features of graphs and enhance models' generalization ability to
unseen distributions. Specifically, InfoIGL introduces a redundancy filter to
compress task-irrelevant information related to environmental factors.
Cooperating with our designed multi-level contrastive learning, we maximize the
mutual information among graphs of the same class in the downstream
classification tasks, preserving invariant features for prediction to a great
extent. An appealing feature of InfoIGL is its strong generalization ability
without depending on supervised signal of invariance. Experiments on both
synthetic and real-world datasets demonstrate that our method achieves
state-of-the-art performance under OOD generalization for graph classification
tasks. The source code is available at https://github.com/maowenyu-11/InfoIGL.
