Graph domain adaptation models are widely adopted in cross-network learning
tasks, with the aim of transferring labeling or structural knowledge.
Currently, there mainly exist two limitations in evaluating graph domain
adaptation models. On one side, they are primarily tested for the specific
cross-network node classification task, leaving tasks at edge-level and
graph-level largely under-explored. Moreover, they are primarily tested in
limited scenarios, such as social networks or citation networks, lacking
validation of model's capability in richer scenarios. As comprehensively
assessing models could enhance model practicality in real-world applications,
we propose a benchmark, known as OpenGDA. It provides abundant pre-processed
and unified datasets for different types of tasks (node, edge, graph). They
originate from diverse scenarios, covering web information systems, urban
systems and natural systems. Furthermore, it integrates state-of-the-art models
with standardized and end-to-end pipelines. Overall, OpenGDA provides a
user-friendly, scalable and reproducible benchmark for evaluating graph domain
adaptation models. The benchmark experiments highlight the challenges of
applying GDA models to real-world applications with consistent good
performance, and potentially provide insights to future research. As an
emerging project, OpenGDA will be regularly updated with new datasets and
models. It could be accessed from this https URL

图领域适应模型在跨网络学习任务中得到广泛应用，目的是传递标记或结构知识。我们提出了一个名为 OpenGDA 的基准，它提供了丰富的预处理和统一数据集以进行不同类型的任务评估，并整合了最先进的模型和标准化的端到端流程。OpenGDA 是一个用户友好、可扩展和可复制的基准，可用于评估图领域适应模型在真实世界应用中的性能和挑战。