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.

对于图外分布问题（OOD），本研究从体系结构角度进行了全面调查，探讨了现代图神经网络的常见构建模块。通过广泛的实验，揭示了图的自注意机制和解耦体系结构对图 ODD 泛化的正面贡献，而线性分类层则会损害图 ODD 泛化能力。此外，我们基于这些发现开发了一种新的图神经网络模型 DGAT，它充分利用了图的自注意机制和解耦体系结构的稳健特性，并通过广泛的实验证明了我们模型在图 ODD 下的有效性，对各种训练策略都展现出了明显和一致的改进。