Out-of-distribution (OOD) generalization is an important issue for Graph
Neural Networks (GNNs). Recent works employ different graph editions to
generate augmented environments and learn an invariant GNN for generalization.
However, the label shift usually occurs in augmentation since graph structural
edition inevitably alters the graph label. This brings inconsistent predictive
relationships among augmented environments, which is harmful to generalization.
To address this issue, we propose \textbf{LiSA}, which generates
label-invariant augmentations to facilitate graph OOD generalization. Instead
of resorting to graph editions, LiSA exploits \textbf{L}abel-\textbf{i}nvariant
\textbf{S}ubgraphs of the training graphs to construct \textbf{A}ugmented
environments. Specifically, LiSA first designs the variational subgraph
generators to extract locally predictive patterns and construct multiple
label-invariant subgraphs efficiently. Then, the subgraphs produced by
different generators are collected to build different augmented environments.
To promote diversity among augmented environments, LiSA further introduces a
tractable energy-based regularization to enlarge pair-wise distances between
the distributions of environments. In this manner, LiSA generates diverse
augmented environments with a consistent predictive relationship and
facilitates learning an invariant GNN. Extensive experiments on node-level and
graph-level OOD benchmarks show that LiSA achieves impressive generalization
performance with different GNN backbones. Code is available on
https://github.com/Samyu0304/LiSA.

LiSA 通过生成标签不变增强的方式解决了图神经网络中图结构编辑带来的标签转换问题，实现了一致且多样化的预测关系来方便图结构的 OOD 泛化。