  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.
