Unsupervised Graph Domain Adaptation (UGDA) aims to transfer knowledge from a
labelled source graph to an unlabelled target graph in order to address the
distribution shifts between graph domains. Previous works have primarily
focused on aligning data from the source and target graph in the representation
space learned by graph neural networks (GNNs). However, the inherent
generalization capability of GNNs has been largely overlooked. Motivated by our
empirical analysis, we reevaluate the role of GNNs in graph domain adaptation
and uncover the pivotal role of the propagation process in GNNs for adapting to
different graph domains. We provide a comprehensive theoretical analysis of
UGDA and derive a generalization bound for multi-layer GNNs. By formulating GNN
Lipschitz for k-layer GNNs, we show that the target risk bound can be tighter
by removing propagation layers in source graph and stacking multiple
propagation layers in target graph. Based on the empirical and theoretical
analysis mentioned above, we propose a simple yet effective approach called
A2GNN for graph domain adaptation. Through extensive experiments on real-world
datasets, we demonstrate the effectiveness of our proposed A2GNN framework.

无监督图域自适应旨在将标记的源图知识转移至未标记的目标图，以解决图域之间的分布偏移。作者重新评估了图域自适应中图神经网络的作用，并发现了传播过程在适应不同图域中的关键作用。基于经验和理论分析，作者提出了名为 A2GNN 的简单而有效的图域自适应方法。通过对真实数据集的广泛实验，作者证明了所提出的 A2GNN 框架的有效性。