Out-of-distribution (OOD) generalization deals with the prevalent learning
scenario where test distribution shifts from training distribution. With rising
application demands and inherent complexity, graph OOD problems call for
specialized solutions. While data-centric methods exhibit performance
enhancements on many generic machine learning tasks, there is a notable absence
of data augmentation methods tailored for graph OOD generalization. In this
work, we propose to achieve graph OOD generalization with the novel design of
non-Euclidean-space linear extrapolation. The proposed augmentation strategy
extrapolates both structure and feature spaces to generate OOD graph data. Our
design tailors OOD samples for specific shifts without corrupting underlying
causal mechanisms. Theoretical analysis and empirical results evidence the
effectiveness of our method in solving target shifts, showing substantial and
constant improvements across various graph OOD tasks.

本文提出了使用非欧氏线性外推的方法来实现图形 OOD 广义化，并在不破坏基本因果机制的情况下为特定转移定制 OOD 样本，理论分析和实证结果表明了我们的方法在解决目标转移问题上的有效性。