{"url":"/dataset/dblp-heterogeneous-node-classification","name":"DBLP (Heterogeneous Node Classification)","full_name":null,"description_markdown":"A popular dataset for node classification on heterogeneous graphs.","description_withheld":null,"homepage":"","introduced_date":"2021-12-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/are-we-really-making-much-progress-revisiting","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","first_author":"Qingsong Lv","url":null},"license":null,"modalities":[],"tasks":[{"name":"Heterogeneous Node Classification","url":"/task/heterogeneous-node-classification","datasets_with_task":"/datasets/task/heterogeneous-node-classification"}],"languages":[],"variants":["DBLP (Heterogeneous Node Classification)"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/heterogeneous-node-classification-on-dblp-2","task":"Heterogeneous Node Classification","dataset_variant":"DBLP (Heterogeneous Node Classification)","rows":11,"metrics":[" Macro-F1","Micro-F1","Macro-F1"],"first_row_in_archive_order":{"model":"RpHGNN","paper":"/paper/efficient-heterogeneous-graph-learning-via","metrics":{" Macro-F1":"95.23","Micro-F1":"95.55"},"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/slotgat-slot-based-message-passing-for","title":"SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network","date":"2024-05-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/are-we-really-making-much-progress-revisiting","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","date":"2021-12-30","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/an-attention-based-graph-neural-network-for","title":"An Attention-based Graph Neural Network for Heterogeneous Structural Learning","date":"2019-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-transformer-networks-1","title":"Graph Transformer Networks","date":"2019-11-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","rows_on_this_dataset":1,"code_links":93,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":106,"samples_ran":50,"samples_unverified":56,"pointer_only_for_licence":43,"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."}},{"paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":58,"samples_ran":31,"samples_unverified":27,"pointer_only_for_licence":22,"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":7,"samples_harvested":220,"samples_ran":103,"samples_unverified":117,"pointer_only_for_licence":88,"papers_with_no_sample_that_ran":1,"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."}