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.

翻译：无监督图域自适应（UGDA）旨在存在域差异的情况下，将知识从标签丰富的源图迁移到无标签的目标图。尽管针对这一新兴任务的方法不断涌现，但由于缺乏标准实验设置与公平的性能比较，理解模型在何种场景下表现优异以及哪些模型表现优异仍具挑战。为填补这一空白，我们提出了首个名为GDABench的无监督图域自适应综合基准，涵盖5个数据集上的16种算法，共计74个自适应任务。通过大量实验，我们观察到当前UGDA模型在不同数据集和自适应场景下的性能差异显著。具体而言，我们认识到当源图与目标图面临显著的分布偏移时，必须制定策略以有效应对和缓解图结构偏移。我们还发现，通过适当的邻域聚合机制，简单的图神经网络变体甚至能够超越最先进的UGDA基线方法。为促进可复现性，我们开发了一个易于使用的库PyGDA，用于训练和评估现有的UGDA方法，为该领域提供了一个标准化平台。我们的源代码与数据集可见于：https://github.com/pygda-team/pygda。