  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.
