  Traditional machine learning methods heavily rely on the independent and
identically distribution assumption, which imposes limitations when the test
distribution deviates from the training distribution. To address this crucial
issue, out-of-distribution (OOD) generalization, which aims to achieve
satisfactory generalization performance when faced with unknown distribution
shifts, has made a significant process. However, the OOD method for
graph-structured data currently lacks clarity and remains relatively unexplored
due to two primary challenges. Firstly, distribution shifts on graphs often
occur simultaneously on node attributes and graph topology. Secondly, capturing
invariant information amidst diverse distribution shifts proves to be a
formidable challenge. To overcome these obstacles, in this paper, we introduce
a novel framework, namely Graph Learning Invariant Domain genERation (GLIDER).
The goal is to (1) diversify variations across domains by modeling the
potential seen or unseen variations of attribute distribution and topological
structure and (2) minimize the discrepancy of the variation in a representation
space where the target is to predict semantic labels. Extensive experiment
results indicate that our model outperforms baseline methods on node-level OOD
generalization across domains in distribution shift on node features and
topological structures simultaneously.
