{"url":"/dataset/reddit-12k","name":"REDDIT-12K","full_name":null,"description_markdown":"Reddit12k contains 11929 graphs each corresponding to an online discussion thread where nodes represent users, and an edge represents the fact that one of the two users responded to the comment of the other user. There is 1 of 11 graph labels associated with each of these 11929 discussion graphs, representing the category of the community.\r\n\r\nSource: [Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity](https://arxiv.org/abs/1904.01098)","description_withheld":null,"homepage":"https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-graph-kernels","title":"Deep Graph Kernels","first_author":"Pinar Yanardag","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Quantization","url":"/task/quantization","datasets_with_task":"/datasets/task/quantization"},{"name":"Topological Data Analysis","url":"/task/topological-data-analysis","datasets_with_task":"/datasets/task/topological-data-analysis"}],"languages":[],"variants":["REDDIT-12K"],"data_loaders":[{"repo":"https://github.com/dmlc/dgl","url":"https://docs.dgl.ai/api/python/dgl.data.html#dgl.data.TUDataset","frameworks":["pytorch","tf","mxnet"]},{"repo":"https://github.com/danielegrattarola/spektral","url":"https://graphneural.network/datasets/#tudataset","frameworks":["tf"]}],"num_papers_in_archive":24,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-classification-on-reddit-12k","task":"Graph Classification","dataset_variant":"REDDIT-12K","rows":1,"metrics":["Accuracy (10 fold)"],"first_row_in_archive_order":{"model":"G-Tuning","paper":"/paper/fine-tuning-graph-neural-networks-by","metrics":{"Accuracy (10 fold)":"42.80"},"code_links":[{"title":"zjunet/G-Tuning","url":"https://github.com/zjunet/G-Tuning"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fine-tuning-graph-neural-networks-by","title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","date":"2023-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":13,"samples_unverified":0,"pointer_only_for_licence":13,"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":1,"samples_harvested":13,"samples_ran":13,"samples_unverified":0,"pointer_only_for_licence":13,"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."}