{"url":"/dataset/reddit-binary","name":"REDDIT-BINARY","full_name":null,"description_markdown":"**REDDIT-BINARY** consists of graphs corresponding to online discussions on Reddit. In each graph, nodes represent users, and there is an edge between them if at least one of them respond to the other’s comment. There are four popular subreddits, namely, IAmA, AskReddit, TrollXChromosomes, and atheism. IAmA and AskReddit are two question/answer based subreddits, and TrollXChromosomes and atheism are two discussion-based subreddits. A graph is labeled according to whether it belongs to a question/answer-based community or a discussion-based community.\r\n\r\nSource: [A simple yet effective baseline for non-attributed graph classification](https://arxiv.org/abs/1811.03508)","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":"Graph Representation Learning","url":"/task/graph-representation-learning","datasets_with_task":"/datasets/task/graph-representation-learning"}],"languages":[],"variants":["REDDIT-B","REDDIT-BINARY"],"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":150,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-classification-on-reddit-binary","task":"Graph Classification","dataset_variant":"REDDIT-BINARY","rows":9,"metrics":["Accuracy","Accuracy (10-fold)"],"first_row_in_archive_order":{"model":"R-GIN + PANDA","paper":"/paper/panda-expanded-width-aware-message-passing","metrics":{"Accuracy":"91.36"},"code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/panda-expanded-width-aware-message-passing","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","date":"2024-06-06","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/strengthening-structural-baselines-for-graph","title":"Strengthening structural baselines for graph classification using Local Topological Profile","date":"2023-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/diffwire-inductive-graph-rewiring-via-the","title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","date":"2022-06-15","rows_on_this_dataset":4,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":7,"samples_unverified":2,"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."}