Mind the Label Shift of Augmentation-based Graph OOD Generalization

• URL: http://arxiv.org/abs/2303.14859v1
• Date: Mon, 27 Mar 2023 00:08:45 GMT
• Title: Mind the Label Shift of Augmentation-based Graph OOD Generalization
• Authors: Junchi Yu and Jian Liang and Ran He
• Abstract summary: LiSA exploits textbfLabel-textbfinvariant textbfSubgraphs of the training graphs to construct textbfAugmented environments. LiSA generates diverse augmented environments with a consistent predictive relationship. Experiments on node-level and graph-level OOD benchmarks show that LiSA achieves impressive generalization performance with different GNN backbones.
• Score: 88.32356432272356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 \url{https://github.com/Samyu0304/LiSA}.

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