{"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/get-component","entry":"get_component","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":8,"n_papers_ran":0,"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":1,"n_samples_ran":0,"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":0,"unverified":1},"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":"2601.18938","paper":"/paper/arxiv-2601-18938","title":"FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ssjcode/FSD-CAP","path":"data_utils.py","file_url":"https://github.com/ssjcode/FSD-CAP/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2305.16618","paper":"/paper/confidence-based-feature-imputation-for","title":"Confidence-Based Feature Imputation for Graphs with Partially Known Features","date":"2023-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daehoum1/pcfi","path":"data_utils.py","file_url":"https://github.com/daehoum1/pcfi/blob/HEAD/data_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":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2212.02374","paper":"/paper/understanding-the-relationship-between-over","title":"On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks","date":"2022-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhonygiraldo/sjlr","path":"load_data/data.py","file_url":"https://github.com/jhonygiraldo/sjlr/blob/HEAD/load_data/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2211.14208","paper":"/paper/gread-graph-neural-reaction-diffusion","title":"GREAD: Graph Neural Reaction-Diffusion Networks","date":"2022-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeongwhanchoi/gread","path":"src/data.py","file_url":"https://github.com/jeongwhanchoi/gread/blob/HEAD/src/data.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":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2209.07754","paper":"/paper/on-the-robustness-of-graph-neural-diffusion","title":"On the Robustness of Graph Neural Diffusion to Topology Perturbations","date":"2022-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zknus/robustness-of-graph-neural-diffusion","path":"data.py","file_url":"https://github.com/zknus/robustness-of-graph-neural-diffusion/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2206.10991","paper":"/paper/graph-neural-networks-as-gradient-flows","title":"Understanding convolution on graphs via energies","date":"2022-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jrowbottomgit/graff","path":"src/data.py","file_url":"https://github.com/jrowbottomgit/graff/blob/HEAD/src/data.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":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"2111.12128","paper":"/paper/on-the-unreasonable-effectiveness-of-feature-1","title":"On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features","date":"2021-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"twitter-research/feature-propagation","path":"src/data_utils.py","file_url":"https://github.com/twitter-research/feature-propagation/blob/HEAD/src/data_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":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}},{"arxiv_id":"1911.05485","paper":"/paper/diffusion-improves-graph-learning-1","title":"Diffusion Improves Graph Learning","date":"2019-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"klicperajo/gdc","path":"data.py","file_url":"https://github.com/klicperajo/gdc/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c68140584cc0e991","mcp_get_code":{"code_sha256":"c68140584cc0e991"}}]}