{"url":"/dataset/opengda","name":"OpenGDA","full_name":null,"description_markdown":"**OpenGDA** is a benchmark for evaluating graph domain adaptation models. 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","description_withheld":null,"homepage":"https://github.com/Skyorca/OpenGDA","introduced_date":"2023-07-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/opengda-graph-domain-adaptation-benchmark-for","title":"OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning","first_author":"Boshen Shi","url":null},"license":{"name":"MIT License","url":"https://github.com/Skyorca/OpenGDA/blob/master/LICENSE"},"modalities":[],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"}],"languages":[],"variants":["OpenGDA"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}