Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world scenarios, this assumption may not always be valid. Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs. Most existing efforts have primarily concentrated on improving graph OOD generalization from two \textbf{model-agnostic} perspectives: data-driven methods and strategy-based learning. However, there has been limited attention dedicated to investigating the impact of well-known \textbf{GNN model architectures} on graph OOD generalization, which is orthogonal to existing research. In this work, we provide the first comprehensive investigation of OOD generalization on graphs from an architecture perspective, by examining the common building blocks of modern GNNs. Through extensive experiments, we reveal that both the graph self-attention mechanism and the decoupled architecture contribute positively to graph OOD generalization. In contrast, we observe that the linear classification layer tends to compromise graph OOD generalization capability. Furthermore, we provide in-depth theoretical insights and discussions to underpin these discoveries. These insights have empowered us to develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture. Extensive experimental results demonstrate the effectiveness of our model under graph OOD, exhibiting substantial and consistent enhancements across various training strategies.

翻译：图神经网络（GNN）在测试数据与训练数据同分布的假设下表现出卓越性能。然而在现实场景中，该假设未必始终成立，因此图上的分布外（OOD）问题日益受到关注。现有研究主要集中于从两种**模型无关**视角（数据驱动方法与基于策略的学习）提升图OOD泛化能力，却鲜有探究广为人知的**GNN模型架构**对图OOD泛化的影响——该方向与现有研究正交。本文首次从架构视角系统研究图上的OOD泛化问题，通过剖析现代GNN的通用构建模块，在大规模实验中揭示了图自注意力机制与解耦架构均能积极促进图OOD泛化，而线性分类层则会削弱图OOD泛化能力。我们进一步为这些发现提供了深入的理论洞见与讨论。基于这些认知，我们开发了新型GNN骨干模型DGAT，该模型融合了图自注意力机制与解耦架构的鲁棒特性。大量实验结果表明，该模型在图OOD场景下具有显著效果，能在多种训练策略中展现持续而实质性的性能提升。