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 https://github.com/Skyorca/OpenGDA.

翻译：图域自适应模型广泛用于跨网络学习任务，旨在迁移标签或结构知识。当前，评估图域自适应模型主要存在两个局限性。一方面，这些模型主要针对特定的跨网络节点分类任务进行测试，而边级和图级任务在很大程度上尚未得到充分探索。另一方面，它们主要在有限场景（如社交网络或引文网络）中进行测试，缺乏在更丰富场景下对模型能力的验证。鉴于全面评估模型可增强模型在实际应用中的实用性，我们提出了一个名为OpenGDA的基准。它为不同类型的任务（节点、边、图）提供了大量预处理的统一数据集，这些数据集来源于多样化的场景，涵盖网络信息系统、城市系统和自然系统。此外，该基准集成了最先进的模型，并配备了标准化、端到端的流程。总体而言，OpenGDA为评估图域自适应模型提供了一个用户友好、可扩展且可复现的基准。基准实验突出了将图域自适应模型应用于实际应用中并保持一致良好性能的挑战，并可能为未来研究提供见解。作为一项新兴项目，OpenGDA将定期更新新的数据集和模型，可通过https://github.com/Skyorca/OpenGDA访问。