{"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/to-dense","entry":"to_dense","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":12,"n_papers_ran":4,"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":10,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":6},"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":"2603.28572","paper":"/paper/arxiv-2603-28572","title":"Unrestrained Simplex Denoising for Discrete Data A Non-Markovian Approach Applied to Graph Generation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"cvignac/DiGress","path":"src/utils.py","file_url":"https://github.com/cvignac/DiGress/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8f656160d633d0b","mcp_get_code":{"code_sha256":"f8f656160d633d0b"}},{"arxiv_id":"2506.15725","paper":null,"title":"arXiv:2506.15725","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"mninniri/GrIDDD","path":"griddd/utils.py","file_url":"https://github.com/mninniri/GrIDDD/blob/HEAD/griddd/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"01fa8f7138b28256","mcp_get_code":{"code_sha256":"01fa8f7138b28256"}},{"arxiv_id":"2406.17341","paper":"/paper/generative-modelling-of-structurally","title":"Generative Modelling of Structurally Constrained Graphs","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manuelmlmadeira/ConStruct","path":"ConStruct/utils.py","file_url":"https://github.com/manuelmlmadeira/ConStruct/blob/HEAD/ConStruct/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"110db6be0f991435","mcp_get_code":{"code_sha256":"110db6be0f991435"}},{"arxiv_id":"2405.11416","paper":"/paper/discrete-state-continuous-time-diffusion-for","title":"Discrete-state Continuous-time Diffusion for Graph Generation","date":"2024-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pricexu/disco","path":"digress_utils.py","file_url":"https://github.com/pricexu/disco/blob/HEAD/digress_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8f656160d633d0b","mcp_get_code":{"code_sha256":"f8f656160d633d0b"}},{"arxiv_id":"2402.16302","paper":"/paper/graph-diffusion-policy-optimization","title":"Graph Diffusion Policy Optimization","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/gdpo","path":"process.py","file_url":"https://github.com/sail-sg/gdpo/blob/HEAD/process.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44c3cf76becae1f4","mcp_get_code":{"code_sha256":"44c3cf76becae1f4"}},{"arxiv_id":"2401.13858","paper":"/paper/inverse-molecular-design-with-multi","title":"Graph Diffusion Transformers for Multi-Conditional Molecular Generation","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liugangcode/MCD","path":"graph_dit/utils.py","file_url":"https://github.com/liugangcode/MCD/blob/HEAD/graph_dit/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea35dc496409db0f","mcp_get_code":{"code_sha256":"ea35dc496409db0f"}},{"arxiv_id":"2312.17397","paper":"/paper/classifier-free-graph-diffusion-for-molecular","title":"Classifier-free graph diffusion for molecular property targeting","date":"2023-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asduffo/freegress","path":"src/utils.py","file_url":"https://github.com/asduffo/freegress/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"04bee8fa958e3921","mcp_get_code":{"code_sha256":"04bee8fa958e3921"}},{"arxiv_id":"2311.02142","paper":"/paper/sparse-training-of-discrete-diffusion-models","title":"Sparse Training of Discrete Diffusion Models for Graph Generation","date":"2023-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qym7/sparsediff","path":"sparse_diffusion/utils.py","file_url":"https://github.com/qym7/sparsediff/blob/HEAD/sparse_diffusion/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fbba1d3a8b6cab5a","mcp_get_code":{"code_sha256":"fbba1d3a8b6cab5a"}},{"arxiv_id":"2305.06102","paper":"/paper/towards-better-graph-representation-learning","title":"Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering","date":"2023-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qslim/PDF","path":"utils/basis_transform.py","file_url":"https://github.com/qslim/PDF/blob/HEAD/utils/basis_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fe73cedf564403e","mcp_get_code":{"code_sha256":"9fe73cedf564403e"}},{"arxiv_id":"2302.09048","paper":"/paper/midi-mixed-graph-and-3d-denoising-diffusion","title":"MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvignac/midi","path":"midi/utils.py","file_url":"https://github.com/cvignac/midi/blob/HEAD/midi/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c431df61de63102e","mcp_get_code":{"code_sha256":"c431df61de63102e"}},{"arxiv_id":"2112.07160","paper":"/paper/improving-spectral-graph-convolution-for","title":"A New Perspective on the Effects of Spectrum in Graph Neural Networks","date":"2021-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qslim/gnn-spectrum","path":"utils/basis_transform.py","file_url":"https://github.com/qslim/gnn-spectrum/blob/HEAD/utils/basis_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fe73cedf564403e","mcp_get_code":{"code_sha256":"9fe73cedf564403e"}},{"arxiv_id":"1912.05783","paper":"/paper/closure-assessing-systematic-generalization","title":"CLOSURE: Assessing Systematic Generalization of CLEVR Models","date":"2019-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raeidsaqur/mgn","path":"mgn/models/graph_matcher.py","file_url":"https://github.com/raeidsaqur/mgn/blob/HEAD/mgn/models/graph_matcher.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d89c0ece4ba3159","mcp_get_code":{"code_sha256":"4d89c0ece4ba3159"}}]}