  Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of
knowledge from a label-rich source graph to an unlabeled target graph under
domain discrepancies. Despite the proliferation of methods designed for this
emerging task, the lack of standard experimental settings and fair performance
comparisons makes it challenging to understand which and when models perform
well across different scenarios. To fill this gap, we present the first
comprehensive benchmark for unsupervised graph domain adaptation named
GDABench, which encompasses 16 algorithms across 5 datasets with 74 adaptation
tasks. Through extensive experiments, we observe that the performance of
current UGDA models varies significantly across different datasets and
adaptation scenarios. Specifically, we recognize that when the source and
target graphs face significant distribution shifts, it is imperative to
formulate strategies to effectively address and mitigate graph structural
shifts. We also find that with appropriate neighbourhood aggregation
mechanisms, simple GNN variants can even surpass state-of-the-art UGDA
baselines. To facilitate reproducibility, we have developed an easy-to-use
library PyGDA for training and evaluating existing UGDA methods, providing a
standardized platform in this community. Our source codes and datasets can be
found at: https://github.com/pygda-team/pygda.
