{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/enzymes/papers/ran/1","list_of":"/dataset/enzymes","dataset":"ENZYMES","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,18],"of":18,"counts":{"papers_with_a_benchmark_row":47,"with_a_code_link":41,"where_syntology_ran_a_sample":18,"not_listed_spam_title":0,"listed":47,"listed_where_code_ran":18,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":18,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":18,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/enzymes/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/panda-expanded-width-aware-message-passing","slug":"panda-expanded-width-aware-message-passing","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","date":"2024-06-06","arxiv_id":"2406.03671","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":3,"official":{"repos":["jeongwhanchoi/panda"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/panda-expanded-width-aware-message-passing#ran","syntology_url":"https://syntology.ai/paper/2406.03671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03671"}}}},{"paper":"/paper/fine-tuning-graph-neural-networks-by","slug":"fine-tuning-graph-neural-networks-by","title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","date":"2023-12-21","arxiv_id":"2312.13583","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":13,"samples_ran":13,"samples_constructed":0,"samples_ran_checked":12,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":13,"official":{"repos":["zjunet/G-Tuning"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/fine-tuning-graph-neural-networks-by#ran","syntology_url":"https://syntology.ai/paper/2312.13583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.13583"}}}},{"paper":"/paper/transitivity-preserving-graph-representation","slug":"transitivity-preserving-graph-representation","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","date":"2023-08-18","arxiv_id":"2308.09517","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":26,"samples_ran":19,"samples_constructed":0,"samples_ran_checked":16,"samples_ran_instrument_failed":3,"samples_unverified":7,"pointer_only_for_licence":0,"official":{"repos":["nslab-cuk/unified-graph-transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/transitivity-preserving-graph-representation#ran","syntology_url":"https://syntology.ai/paper/2308.09517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09517"}}}},{"paper":"/paper/graph-trees-with-attention","slug":"graph-trees-with-attention","title":"TREE-G: Decision Trees Contesting Graph Neural Networks","date":"2022-07-06","arxiv_id":"2207.02760","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["mayabechlerspeicher/tree-g"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/graph-trees-with-attention#ran","syntology_url":"https://syntology.ai/paper/2207.02760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02760"}}}},{"paper":"/paper/recipe-for-a-general-powerful-scalable-graph","slug":"recipe-for-a-general-powerful-scalable-graph","title":"Recipe for a General, Powerful, Scalable Graph Transformer","date":"2022-05-25","arxiv_id":"2205.12454","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":21,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":2,"samples_unverified":18,"pointer_only_for_licence":0,"official":{"repos":["rampasek/GraphGPS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/recipe-for-a-general-powerful-scalable-graph#ran","syntology_url":"https://syntology.ai/paper/2205.12454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.12454"}}}},{"paper":"/paper/improving-spectral-graph-convolution-for","slug":"improving-spectral-graph-convolution-for","title":"A New Perspective on the Effects of Spectrum in Graph Neural Networks","date":"2021-12-14","arxiv_id":"2112.07160","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["qslim/gnn-spectrum"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/improving-spectral-graph-convolution-for#ran","syntology_url":"https://syntology.ai/paper/2112.07160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.07160"}}}},{"paper":"/paper/dropgnn-random-dropouts-increase-the","slug":"dropgnn-random-dropouts-increase-the","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","date":"2021-11-11","arxiv_id":"2111.06283","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["karolismart/dropgnn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/dropgnn-random-dropouts-increase-the#ran","syntology_url":"https://syntology.ai/paper/2111.06283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06283"}}}},{"paper":"/paper/how-attentive-are-graph-attention-networks","slug":"how-attentive-are-graph-attention-networks","title":"How Attentive are Graph Attention Networks?","date":"2021-05-30","arxiv_id":"2105.14491","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":16,"samples_ran":11,"samples_constructed":1,"samples_ran_checked":11,"samples_ran_instrument_failed":0,"samples_unverified":5,"pointer_only_for_licence":1,"official":{"repos":["tech-srl/how_attentive_are_gats"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/how-attentive-are-graph-attention-networks#ran","syntology_url":"https://syntology.ai/paper/2105.14491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14491"}}}},{"paper":"/paper/principal-neighbourhood-aggregation-for-graph","slug":"principal-neighbourhood-aggregation-for-graph","title":"Principal Neighbourhood Aggregation for Graph Nets","date":"2020-04-12","arxiv_id":"2004.05718","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":55,"samples_ran":33,"samples_constructed":4,"samples_ran_checked":11,"samples_ran_instrument_failed":22,"samples_unverified":22,"pointer_only_for_licence":48,"official":{"repos":["lukecavabarrett/pna"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/principal-neighbourhood-aggregation-for-graph#ran","syntology_url":"https://syntology.ai/paper/2004.05718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.05718"}}}},{"paper":"/paper/hierarchical-representation-learning-in-graph","slug":"hierarchical-representation-learning-in-graph","title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","date":"2019-10-24","arxiv_id":"1910.11436","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":1,"official":{"repos":["danielegrattarola/decimation-pooling"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hierarchical-representation-learning-in-graph#ran","syntology_url":"https://syntology.ai/paper/1910.11436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11436"}}}},{"paper":"/paper/improving-attention-mechanism-in-graph-neural","slug":"improving-attention-mechanism-in-graph-neural","title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation","date":"2019-07-04","arxiv_id":"1907.02204","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["zetayue/CPA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/improving-attention-mechanism-in-graph-neural#ran","syntology_url":"https://syntology.ai/paper/1907.02204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02204"}}}},{"paper":"/paper/graph-star-net-for-generalized-multi-task-1","slug":"graph-star-net-for-generalized-multi-task-1","title":"Graph Star Net for Generalized Multi-Task Learning","date":"2019-06-21","arxiv_id":"1906.12330","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":6,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":1,"samples_unverified":4,"pointer_only_for_licence":1,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/graph-star-net-for-generalized-multi-task-1#ran","syntology_url":"https://syntology.ai/paper/1906.12330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.12330"}}}},{"paper":"/paper/wasserstein-weisfeiler-lehman-graph-kernels","slug":"wasserstein-weisfeiler-lehman-graph-kernels","title":"Wasserstein Weisfeiler-Lehman Graph Kernels","date":"2019-06-04","arxiv_id":"1906.01277","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":11,"samples_ran":5,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":0,"samples_unverified":6,"pointer_only_for_licence":0,"official":{"repos":["BorgwardtLab/WWL"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/wasserstein-weisfeiler-lehman-graph-kernels#ran","syntology_url":"https://syntology.ai/paper/1906.01277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01277"}}}},{"paper":"/paper/how-powerful-are-graph-neural-networks","slug":"how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","arxiv_id":"1810.00826","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":6,"samples_constructed":2,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":4,"pointer_only_for_licence":5,"official":{"repos":["weihua916/powerful-gnns"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/how-powerful-are-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1810.00826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00826"}}}},{"paper":"/paper/hierarchical-graph-representation-learning","slug":"hierarchical-graph-representation-learning","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","date":"2018-06-22","arxiv_id":"1806.08804","rows_on_this_dataset":2,"code_links":14,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":20,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":10,"pointer_only_for_licence":0,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/hierarchical-graph-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1806.08804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08804"}}}},{"paper":"/paper/optimal-transport-for-structured-data-with","slug":"optimal-transport-for-structured-data-with","title":"Optimal Transport for structured data with application on graphs","date":"2018-05-23","arxiv_id":"1805.09114","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":3,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/optimal-transport-for-structured-data-with#ran","syntology_url":"https://syntology.ai/paper/1805.09114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09114"}}}},{"paper":"/paper/graph-attention-networks","slug":"graph-attention-networks","title":"Graph Attention Networks","date":"2017-10-30","arxiv_id":"1710.10903","rows_on_this_dataset":1,"code_links":93,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":106,"samples_ran":61,"samples_constructed":28,"samples_ran_checked":52,"samples_ran_instrument_failed":9,"samples_unverified":45,"pointer_only_for_licence":46,"official":{"repos":["PetarV-/GAT"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/graph-attention-networks#ran","syntology_url":"https://syntology.ai/paper/1710.10903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.10903"}}}},{"paper":"/paper/semi-supervised-classification-with-graph","slug":"semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","arxiv_id":"1609.02907","rows_on_this_dataset":1,"code_links":55,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":58,"samples_ran":39,"samples_constructed":13,"samples_ran_checked":34,"samples_ran_instrument_failed":5,"samples_unverified":19,"pointer_only_for_licence":23,"official":{"repos":["tkipf/pygcn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/semi-supervised-classification-with-graph#ran","syntology_url":"https://syntology.ai/paper/1609.02907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.02907"}}}}],"record_sha256":"6e5c62c137590834f207342bfe0373486e56c4ae0d188a2e97341f35aabdcecd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}