We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for causal subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization.

翻译：我们致力于解决图分布外（OOD）泛化问题。现有图OOD算法要么依赖严格假设，要么未能充分利用训练数据中的环境信息。本文提出同时纳入标签与环境因果独立性（LECI），以充分挖掘标签与环境信息，从而应对先前方法在识别因果不变子图时面临的挑战。我们进一步开发了一种对抗训练策略，联合优化这两个属性以实现具有理论保证的因果子图发现。大量实验与分析表明，LECI在合成数据集与真实数据集上均显著优于先前方法，确立了LECI作为图OOD泛化问题的可行且高效解决方案的地位。