  Graph neural networks (GNNs) have demonstrated remarkable success in numerous
graph analytical tasks. Yet, their effectiveness is often compromised in
real-world scenarios due to distribution shifts, limiting their capacity for
knowledge transfer across changing environments or domains. Recently,
Unsupervised Graph Domain Adaptation (UGDA) has been introduced to resolve this
issue. UGDA aims to facilitate knowledge transfer from a labeled source graph
to an unlabeled target graph. Current UGDA efforts primarily focus on
model-centric methods, such as employing domain invariant learning strategies
and designing model architectures. However, our critical examination reveals
the limitations inherent to these model-centric methods, while a data-centric
method allowed to modify the source graph provably demonstrates considerable
potential. This insight motivates us to explore UGDA from a data-centric
perspective. By revisiting the theoretical generalization bound for UGDA, we
identify two data-centric principles for UGDA: alignment principle and
rescaling principle. Guided by these principles, we propose GraphAlign, a novel
UGDA method that generates a small yet transferable graph. By exclusively
training a GNN on this new graph with classic Empirical Risk Minimization
(ERM), GraphAlign attains exceptional performance on the target graph.
Extensive experiments under various transfer scenarios demonstrate the
GraphAlign outperforms the best baselines by an average of 2.16%, training on
the generated graph as small as 0.25~1% of the original training graph.
