{"url":"/dataset/tsp-hcp-benchmark-set","name":"TSP/HCP Benchmark set","full_name":null,"description_markdown":"This is a benchmark set for Traveling salesman problem (TSP) with characteristics that are different from the existing benchmark sets. In particular, it focuses on small instances which prove to be challenging for one or more state-of-the-art TSP algorithms. These instances are based on difficult instances of Hamiltonian cycle problem (HCP). This includes instances from literature, specially modified randomly generated instances, and instances arising from the conversion of other difficult problems to HCP.","description_withheld":null,"homepage":"https://sites.flinders.edu.au/flinders-hamiltonian-cycle-project/tsp-and-hcp-benchmark-set/","introduced_date":"2018-06-25","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A new benchmark set for Traveling salesman problem and Hamiltonian cycle problem","first_author":null,"url":null},"license":null,"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Traveling Salesman Problem","url":"/task/traveling-salesman-problem","datasets_with_task":"/datasets/task/traveling-salesman-problem"}],"languages":[],"variants":["TSP/HCP Benchmark set"],"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/link-prediction-on-tsp-hcp-benchmark-set","task":"Link Prediction","dataset_variant":"TSP/HCP Benchmark set","rows":4,"metrics":["F1"],"first_row_in_archive_order":{"model":"TGT-Agx4","paper":"/paper/triplet-interaction-improves-graph","metrics":{"F1":"0.871"},"code_links":[{"title":"shamim-hussain/egt_pytorch","url":"https://github.com/shamim-hussain/egt_pytorch"},{"title":"shamim-hussain/tgt","url":"https://github.com/shamim-hussain/tgt"},{"title":"shamim-hussain/egt_triangular","url":"https://github.com/shamim-hussain/egt_triangular"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/triplet-interaction-improves-graph","title":"Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers","date":"2024-02-07","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":10,"samples_unverified":3,"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/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":3,"samples_harvested":40,"samples_ran":11,"samples_unverified":29,"pointer_only_for_licence":0,"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."}