Graph Structure and Feature Extrapolation for Out-of-Distribution
  Generalization
Xiner Li Shurui Gui Youzhi Luo Shuiwang Ji
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.