{"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":"/task/graph-similarity/papers/ran/1","list_of":"/task/graph-similarity","task":"Graph Similarity","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_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'","n_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)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","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 task or check it against the task'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.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,10],"of":10,"counts":{"archive_papers_tagged":113,"with_a_code_link":51,"where_syntology_ran_a_sample":10,"not_listed_spam_title":0,"listed":113,"listed_where_code_ran":10,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":10,"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 the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/graph-similarity/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/quality-measures-for-dynamic-graph-generative","slug":"quality-measures-for-dynamic-graph-generative","title":"Quality Measures for Dynamic Graph Generative Models","date":"2025-03-03","arxiv_id":"2503.01720","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":1,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quality-measures-for-dynamic-graph-generative#ran","syntology_url":"https://syntology.ai/paper/2503.01720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.01720"}},"official":{"repos":["ryienh/jl-metric"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/covered-forest-fine-grained-generalization","slug":"covered-forest-fine-grained-generalization","title":"Covered Forest: Fine-grained generalization analysis of graph neural networks","date":"2024-12-10","arxiv_id":"2412.07106","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/covered-forest-fine-grained-generalization#ran","syntology_url":"https://syntology.ai/paper/2412.07106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.07106"}},"official":{"repos":["benfinkelshtein/CoveredForests"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-graph-similarity-computation-with","slug":"efficient-graph-similarity-computation-with","title":"Efficient Graph Similarity Computation with Alignment Regularization","date":"2024-06-21","arxiv_id":"2406.14929","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":3,"n_ran_checked":11,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":16,"phrase":"12 ran (of which 3 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/efficient-graph-similarity-computation-with#ran","syntology_url":"https://syntology.ai/paper/2406.14929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14929"}},"official":{"repos":["jhuow/eric"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":3,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/structure-your-data-towards-semantic-graph","slug":"structure-your-data-towards-semantic-graph","title":"Structure Your Data: Towards Semantic Graph Counterfactuals","date":"2024-03-11","arxiv_id":"2403.06514","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/structure-your-data-towards-semantic-graph#ran","syntology_url":"https://syntology.ai/paper/2403.06514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06514"}},"official":{"repos":["aggeliki-dimitriou/sgce"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-neural-framework-for-learning-subgraph-and","slug":"a-neural-framework-for-learning-subgraph-and","title":"GREED: A Neural Framework for Learning Graph Distance Functions","date":"2021-12-24","arxiv_id":"2112.13143","repositories_listed":2,"syntology":{"n":15,"n_ran":6,"n_constructed":2,"n_ran_checked":6,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/a-neural-framework-for-learning-subgraph-and#ran","syntology_url":"https://syntology.ai/paper/2112.13143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13143"}},"official":{"repos":["idea-iitd/neurosed","idea-iitd/greed"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":6,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-graph-based-place-recognition-for-3d","slug":"semantic-graph-based-place-recognition-for-3d","title":"Semantic Graph Based Place Recognition for 3D Point Clouds","date":"2020-08-26","arxiv_id":"2008.11459","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-graph-based-place-recognition-for-3d#ran","syntology_url":"https://syntology.ai/paper/2008.11459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11459"}},"official":{"repos":["kxhit/SG_PR"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-graph-matching-networks-for-deep-1","slug":"hierarchical-graph-matching-networks-for-deep-1","title":"Multilevel Graph Matching Networks for Deep Graph Similarity Learning","date":"2020-07-08","arxiv_id":"2007.04395","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hierarchical-graph-matching-networks-for-deep-1#ran","syntology_url":"https://syntology.ai/paper/2007.04395","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04395"}},"official":{"repos":["kleincup/MGMN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cldice-a-topology-preserving-loss-function","slug":"cldice-a-topology-preserving-loss-function","title":"clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation","date":"2020-03-16","arxiv_id":"2003.07311","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cldice-a-topology-preserving-loss-function#ran","syntology_url":"https://syntology.ai/paper/2003.07311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.07311"}},"official":{"repos":["jocpae/clDice"],"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":["listed","official"]}}},{"url":"/paper/generalized-label-propagation-methods-for","slug":"generalized-label-propagation-methods-for","title":"Label Efficient Semi-Supervised Learning via Graph Filtering","date":"2019-01-28","arxiv_id":"1901.09993","repositories_listed":1,"syntology":{"n":10,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/generalized-label-propagation-methods-for#ran","syntology_url":"https://syntology.ai/paper/1901.09993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09993"}},"official":{"repos":["liqimai/Efficient-SSL"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/distance-metric-learning-using-graph","slug":"distance-metric-learning-using-graph","title":"Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks","date":"2017-03-07","arxiv_id":"1703.02161","repositories_listed":3,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/distance-metric-learning-using-graph#ran","syntology_url":"https://syntology.ai/paper/1703.02161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.02161"}},"official":{"repos":["sk1712/gcn_metric_learning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}}],"record_sha256":"8ed1b23e0232a5a445632a19d486a91780298af7fa3ee8c0784cc0d17b3c6ab9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}