{"url":"/dataset/oag-l1-field","name":"OAG-L1-Field","full_name":null,"description_markdown":"A popular dataset for node classification on heterogeneous graphs.","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Heterogeneous Node Classification","url":"/task/heterogeneous-node-classification","datasets_with_task":"/datasets/task/heterogeneous-node-classification"}],"languages":[],"variants":["OAG-L1-Field"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/heterogeneous-node-classification-on-oag-l1","task":"Heterogeneous Node Classification","dataset_variant":"OAG-L1-Field","rows":5,"metrics":["NDCG","MRR"],"first_row_in_archive_order":{"model":"RpHGNN","paper":"/paper/efficient-heterogeneous-graph-learning-via","metrics":{"MRR":"86.79","NDCG":"87.80"},"code_links":[{"title":"CrawlScript/RpHGNN","url":"https://github.com/CrawlScript/RpHGNN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-heterogeneous-graph-learning-via","title":"Efficient Heterogeneous Graph Learning via Random Projection","date":"2023-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simple-and-efficient-heterogeneous-graph","title":"Simple and Efficient Heterogeneous Graph Neural Network","date":"2022-07-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/scalable-graph-neural-networks-for-1","title":"Scalable Graph Neural Networks for Heterogeneous Graphs","date":"2020-11-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/heterogeneous-graph-transformer","title":"Heterogeneous Graph Transformer","date":"2020-03-03","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/modeling-relational-data-with-graph","title":"Modeling Relational Data with Graph Convolutional Networks","date":"2017-03-17","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":32,"samples_ran":10,"samples_unverified":22,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":46,"samples_ran":16,"samples_unverified":30,"pointer_only_for_licence":23,"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."}