{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/graph-2","entry":"graph","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":6,"n_papers_ran":1,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":8,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":7},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2309.16746","paper":"/paper/implicit-gaussian-process-representation-of","title":"Implicit Gaussian process representation of vector fields over arbitrary latent manifolds","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agosztolai/rvgp","path":"RVGP/plotting.py","file_url":"https://github.com/agosztolai/rvgp/blob/HEAD/RVGP/plotting.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"748d359372c35836","mcp_get_code":{"code_sha256":"748d359372c35836"}},{"arxiv_id":"2304.03376","paper":"/paper/interpretable-statistical-representations-of","title":"Interpretable statistical representations of neural population dynamics and geometry","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Dynamics-of-Neural-Systems-Lab/MARBLE","path":"MARBLE/plotting.py","file_url":"https://github.com/Dynamics-of-Neural-Systems-Lab/MARBLE/blob/HEAD/MARBLE/plotting.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"065d0cb22322a697","mcp_get_code":{"code_sha256":"065d0cb22322a697"}},{"arxiv_id":"2008.04838","paper":"/paper/transnet-v2-an-effective-deep-network","title":"TransNet V2: An effective deep network architecture for fast shot transition detection","date":"2020-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"soCzech/TransNetV2","path":"training/metrics_utils.py","file_url":"https://github.com/soCzech/TransNetV2/blob/HEAD/training/metrics_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c19395bc49d7798c","mcp_get_code":{"code_sha256":"c19395bc49d7798c"}},{"arxiv_id":"2003.00982","paper":"/paper/benchmarking-graph-neural-networks","title":"Benchmarking Graph Neural Networks","date":"2020-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liketheflower/jimmy_dgl","path":"python/dgl/convert.py","file_url":"https://github.com/liketheflower/jimmy_dgl/blob/HEAD/python/dgl/convert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5759d6efe4e482a4","mcp_get_code":{"code_sha256":"5759d6efe4e482a4"}},{"arxiv_id":"1909.01315","paper":"/paper/deep-graph-library-towards-efficient-and","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","date":"2019-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"classicsong/dgl","path":"python/dgl/convert.py","file_url":"https://github.com/classicsong/dgl/blob/HEAD/python/dgl/convert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8531b4232412ff4e","mcp_get_code":{"code_sha256":"8531b4232412ff4e"}},{"arxiv_id":"1909.01315","paper":"/paper/deep-graph-library-towards-efficient-and","title":"Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks","date":"2019-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mengyangniu/dgl","path":"python/dgl/convert.py","file_url":"https://github.com/mengyangniu/dgl/blob/HEAD/python/dgl/convert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0d2f6edf7894499d","mcp_get_code":{"code_sha256":"0d2f6edf7894499d"}},{"arxiv_id":"1907.03382","paper":"/paper/etalumis-bringing-probabilistic-programming","title":"Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale","date":"2019-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pyprob/pyprob","path":"pyprob/diagnostics.py","file_url":"https://github.com/pyprob/pyprob/blob/HEAD/pyprob/diagnostics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"172d80fac10ddeab","mcp_get_code":{"code_sha256":"172d80fac10ddeab"}},{"arxiv_id":"1907.03382","paper":"/paper/etalumis-bringing-probabilistic-programming","title":"Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale","date":"2019-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"probprog/pyprob","path":"pyprob/diagnostics.py","file_url":"https://github.com/probprog/pyprob/blob/HEAD/pyprob/diagnostics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"ecfe4169183d740c","mcp_get_code":{"code_sha256":"ecfe4169183d740c"}}]}