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.

翻译：分布偏移问题正逐渐成为图表示学习中的一个关键关注点。从不变学习和稳定学习的视角来看，作为近期建立的分布外泛化范式，图的稳定特征被假设为因果性地决定标签，而环境特征往往不稳定，并可能导致两种主要类型的分布偏移。相关性偏移通常由训练数据和测试数据中环境特征与标签之间的虚假相关性差异引起；协变量偏移则常源于测试数据中出现新的环境特征。然而，大多数策略（如不变学习或图增强）通常受限于有限的训练环境或扰动的稳定特征，从而在处理协变量偏移问题时暴露出局限性。为应对这一挑战，我们提出了一种简单而有效的数据增强策略——对抗性不变增强（AIA），以处理图上的协变量偏移。具体而言，给定训练数据，AIA旨在外推并生成新环境，同时在增强过程中保留原始稳定特征。这种设计使图分类模型具备在新环境中识别稳定特征的增强能力，从而有效应对数据中的协变量偏移。大量实验及深入实证分析证明了我们方法的优越性。实现代码已公开发布在 https://github.com/yongduosui/AIA。