{"url":"/dataset/cluster","name":"CLUSTER","full_name":null,"description_markdown":"CLUSTER is a node classification tasks generated with [Stochastic Block Models](https://paperswithcode.com/paper/community-detection-and-stochastic-block), which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. CLUSTER aims at identifying community clusters in a semi-supervised setting.\r\n\r\n\r\nSource: [Benchmarking Graph Neural Networks](https://arxiv.org/pdf/2003.00982v3.pdf)","description_withheld":null,"homepage":"","introduced_date":"2020-03-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-graph-neural-networks","title":"Benchmarking Graph Neural Networks","first_author":"Vijay Prakash Dwivedi","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Node Classification","url":"/task/node-classification","datasets_with_task":"/datasets/task/node-classification"}],"languages":[],"variants":["CLUSTER"],"data_loaders":[],"num_papers_in_archive":159,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/node-classification-on-cluster","task":"Node Classification","dataset_variant":"CLUSTER","rows":12,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GRIT","paper":"/paper/graph-inductive-biases-in-transformers","metrics":{"Accuracy":"80.026"},"code_links":[{"title":"liamma/grit","url":"https://github.com/liamma/grit"},{"title":"linusbao/MoSE","url":"https://github.com/linusbao/MoSE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unlocking-the-potential-of-classic-gnns-for","title":"Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence","date":"2025-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-long-range-dependencies-on-graphs","title":"Learning Long Range Dependencies on Graphs via Random Walks","date":"2024-06-05","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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ckgconv-general-graph-convolution-with","title":"CKGConv: General Graph Convolution with Continuous Kernels","date":"2024-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/topology-informed-graph-transformer","title":"Topology-Informed Graph Transformer","date":"2024-02-03","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":14,"samples_unverified":7,"pointer_only_for_licence":21,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-transformers-without-positional","title":"Graph Transformers without Positional Encodings","date":"2024-01-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graph-inductive-biases-in-transformers","title":"Graph Inductive Biases in Transformers without Message Passing","date":"2023-05-27","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":6,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-propagation-transformer-for-graph","title":"Graph Propagation Transformer for Graph Representation Learning","date":"2023-05-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exphormer-sparse-transformers-for-graphs","title":"Exphormer: Sparse Transformers for Graphs","date":"2023-03-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/automatic-relation-aware-graph-network","title":"Automatic Relation-aware Graph Network Proliferation","date":"2022-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/recipe-for-a-general-powerful-scalable-graph","title":"Recipe for a General, Powerful, Scalable Graph Transformer","date":"2022-05-25","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":3,"samples_unverified":18,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/edge-augmented-graph-transformers-global-self","title":"Global Self-Attention as a Replacement for Graph Convolution","date":"2021-08-07","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/benchmarking-graph-neural-networks","title":"Benchmarking Graph Neural Networks","date":"2020-03-02","rows_on_this_dataset":1,"code_links":15,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":1,"samples_unverified":22,"pointer_only_for_licence":0,"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":10,"samples_harvested":126,"samples_ran":54,"samples_unverified":72,"pointer_only_for_licence":40,"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."}