  The issue of distribution shifts is emerging as a critical concern in graph
representation learning. From the perspective of invariant learning and stable
learning, a recently well-established paradigm for out-of-distribution
generalization, stable features of the graph are assumed to causally determine
labels, while environmental features tend to be unstable and can lead to the
two primary types of distribution shifts. The correlation shift is often caused
by the spurious correlation between environmental features and labels that
differs between the training and test data; the covariate shift often stems
from the presence of new environmental features in test data. However, most
strategies, such as invariant learning or graph augmentation, typically
struggle with limited training environments or perturbed stable features, thus
exposing limitations in handling the problem of covariate shift. To address
this challenge, we propose a simple-yet-effective data augmentation strategy,
Adversarial Invariant Augmentation (AIA), to handle the covariate shift on
graphs. Specifically, given the training data, AIA aims to extrapolate and
generate new environments, while concurrently preserving the original stable
features during the augmentation process. Such a design equips the graph
classification model with an enhanced capability to identify stable features in
new environments, thereby effectively tackling the covariate shift in data.
Extensive experiments with in-depth empirical analysis demonstrate the
superiority of our approach. The implementation codes are publicly available at
https://github.com/yongduosui/AIA.
