{"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/load-feat","entry":"load_feat","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":5,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2607.05095","paper":"/paper/arxiv-2607-05095","title":"FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"NoneBone/FAST","path":"utils.py","file_url":"https://github.com/NoneBone/FAST/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bffbe42c7216ef02","mcp_get_code":{"code_sha256":"bffbe42c7216ef02"}},{"arxiv_id":"2211.08568","paper":"/paper/graph-sequential-neural-ode-process-for-link","title":"Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs","date":"2022-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rmanluo/gsnop","path":"code/utils.py","file_url":"https://github.com/rmanluo/gsnop/blob/HEAD/code/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7022c11f6017daad","mcp_get_code":{"code_sha256":"7022c11f6017daad"}},{"arxiv_id":"2203.14883","paper":"/paper/tgl-a-general-framework-for-temporal-gnn","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-research/tgl","path":"utils.py","file_url":"https://github.com/amazon-research/tgl/blob/HEAD/utils.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":"5e7b890ce395b0ab","mcp_get_code":{"code_sha256":"5e7b890ce395b0ab"}},{"arxiv_id":"2004.01024","paper":"/paper/modeling-dynamic-heterogeneous-network-for","title":"Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN","date":"2020-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skx300/DyHATR","path":"src/utils/data_helper.py","file_url":"https://github.com/skx300/DyHATR/blob/HEAD/src/utils/data_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e790950168ba3c34","mcp_get_code":{"code_sha256":"e790950168ba3c34"}},{"arxiv_id":"1803.00839","paper":"/paper/pose-robust-face-recognition-via-deep","title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","date":"2018-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"penincillin/DREAM","path":"src/CFP/eval_roc.py","file_url":"https://github.com/penincillin/DREAM/blob/HEAD/src/CFP/eval_roc.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":"e53e7c8483ea523a","mcp_get_code":{"code_sha256":"e53e7c8483ea523a"}}]}