{"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/build-graph","entry":"build_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":20,"n_papers_ran":5,"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":24,"n_samples_ran":6,"n_samples_fingerprinted":1,"n_places":24,"n_places_pointer_only":5,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"unverified":18},"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":"2606.12748","paper":"/paper/arxiv-2606-12748","title":"Agent-based models for the evolution of morphological alternation patterns","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"SakanaAI/LanguageEvolution","path":"core/network.py","file_url":"https://github.com/SakanaAI/LanguageEvolution/blob/HEAD/core/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad65e71749457c90","mcp_get_code":{"code_sha256":"ad65e71749457c90"}},{"arxiv_id":"2506.08618","paper":"/paper/2506-08618","title":"HSG-12M: A Large-Scale Spatial Multigraph Dataset","date":"2025-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sarinstein-yan/poly2graph","path":"src/poly2graph/skeleton2graph.py","file_url":"https://github.com/sarinstein-yan/poly2graph/blob/HEAD/src/poly2graph/skeleton2graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ccd76565fb92dd4","mcp_get_code":{"code_sha256":"3ccd76565fb92dd4"}},{"arxiv_id":"2504.12764","paper":"/paper/graphomni-a-comprehensive-and-extendable","title":"GraphOmni: A Comprehensive and Extendable Benchmark Framework for Large Language Models on Graph-theoretic Tasks","date":"2025-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gai-community/graphomni","path":"eval_fun/verification.py","file_url":"https://github.com/gai-community/graphomni/blob/HEAD/eval_fun/verification.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83b83cb8a93f63f4","mcp_get_code":{"code_sha256":"83b83cb8a93f63f4"}},{"arxiv_id":"2309.06719","paper":"/paper/trafficgpt-viewing-processing-and-interacting","title":"TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijlansg/trafficgpt","path":"LLMAgent/buildGraph.py","file_url":"https://github.com/lijlansg/trafficgpt/blob/HEAD/LLMAgent/buildGraph.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"2e1abc2b73658c62","mcp_get_code":{"code_sha256":"2e1abc2b73658c62"}},{"arxiv_id":"2308.04142","paper":"/paper/class-level-structural-relation-modelling-and","title":"Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"czt117/csrms","path":"graph/calcu_graph.py","file_url":"https://github.com/czt117/csrms/blob/HEAD/graph/calcu_graph.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe906cf5793628af","mcp_get_code":{"code_sha256":"fe906cf5793628af"}},{"arxiv_id":"2307.02759","paper":"/paper/knowledge-graph-self-supervised","title":"Knowledge Graph Self-Supervised Rationalization for Recommendation","date":"2023-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkuds/kgrec","path":"utils/data_loader.py","file_url":"https://github.com/hkuds/kgrec/blob/HEAD/utils/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"44c2871f40d5a80b","mcp_get_code":{"code_sha256":"44c2871f40d5a80b"}},{"arxiv_id":"2307.02759","paper":"/paper/knowledge-graph-self-supervised","title":"Knowledge Graph Self-Supervised Rationalization for Recommendation","date":"2023-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkuds/kgrec","path":"utils/data_loader_kgcl.py","file_url":"https://github.com/hkuds/kgrec/blob/HEAD/utils/data_loader_kgcl.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a6bc535edeabcd18","mcp_get_code":{"code_sha256":"a6bc535edeabcd18"}},{"arxiv_id":"2303.08682","paper":"/paper/rsfnet-a-white-box-image-retouching-approach","title":"RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vicky0522/RSFNet","path":"basicsr/archs/rsfnet_arch.py","file_url":"https://github.com/Vicky0522/RSFNet/blob/HEAD/basicsr/archs/rsfnet_arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"ba145379f28e49f9","mcp_get_code":{"code_sha256":"ba145379f28e49f9"}},{"arxiv_id":"2007.00145","paper":"/paper/modality-agnostic-attention-fusion-for-visual","title":"Modality-Agnostic Attention Fusion for visual search with text feedback","date":"2020-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nashory/rtic-gcn-pytorch","path":"utils/gcn_utils.py","file_url":"https://github.com/nashory/rtic-gcn-pytorch/blob/HEAD/utils/gcn_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70e3fde283039da4","mcp_get_code":{"code_sha256":"70e3fde283039da4"}},{"arxiv_id":"2006.07556","paper":"/paper/neural-architecture-search-using-bayesian","title":"Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels","date":"2020-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingchenwan/nasbowl","path":"loader.py","file_url":"https://github.com/xingchenwan/nasbowl/blob/HEAD/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a97c65c41cd8219","mcp_get_code":{"code_sha256":"2a97c65c41cd8219"}},{"arxiv_id":"2005.02844","paper":"/paper/tagnn-target-attentive-graph-neural-networks","title":"TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation","date":"2020-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CRIPAC-DIG/TAGNN","path":"utils.py","file_url":"https://github.com/CRIPAC-DIG/TAGNN/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":"62fa0eabd16730ce","mcp_get_code":{"code_sha256":"62fa0eabd16730ce"}},{"arxiv_id":"1912.11684","paper":"/paper/look-listen-and-act-towards-audio-visual","title":"Look, Listen, and Act: Towards Audio-Visual Embodied Navigation","date":"2019-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhang-yw/avn","path":"code/navigate.py","file_url":"https://github.com/zhang-yw/avn/blob/HEAD/code/navigate.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70989651ea691578","mcp_get_code":{"code_sha256":"70989651ea691578"}},{"arxiv_id":"1910.06262","paper":"/paper/restoring-ancient-text-using-deep-learning-a","title":"Restoring ancient text using deep learning: a case study on Greek epigraphy","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sommerschield/ancient-text-restoration","path":"pythia/model/graph.py","file_url":"https://github.com/sommerschield/ancient-text-restoration/blob/HEAD/pythia/model/graph.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":"dcd412c5340b7001","mcp_get_code":{"code_sha256":"dcd412c5340b7001"}},{"arxiv_id":"1904.01569","paper":"/paper/exploring-randomly-wired-neural-networks-for","title":"Exploring Randomly Wired Neural Networks for Image Recognition","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hebo1221/RandWireNN","path":"utils/graph.py","file_url":"https://github.com/hebo1221/RandWireNN/blob/HEAD/utils/graph.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":"7a664040a2491c18","mcp_get_code":{"code_sha256":"7a664040a2491c18"}},{"arxiv_id":"1807.03100","paper":"/paper/robust-text-to-sql-generation-with-execution","title":"Robust Text-to-SQL Generation with Execution-Guided Decoding","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/PointerSQL","path":"model/pointer_net_graph.py","file_url":"https://github.com/Microsoft/PointerSQL/blob/HEAD/model/pointer_net_graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfad29b0a7e82227","mcp_get_code":{"code_sha256":"cfad29b0a7e82227"}},{"arxiv_id":"1807.03100","paper":"/paper/robust-text-to-sql-generation-with-execution","title":"Robust Text-to-SQL Generation with Execution-Guided Decoding","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/PointerSQL","path":"model/pointer_net_graph_meta.py","file_url":"https://github.com/Microsoft/PointerSQL/blob/HEAD/model/pointer_net_graph_meta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a7354677696eb19","mcp_get_code":{"code_sha256":"6a7354677696eb19"}},{"arxiv_id":"1803.03764","paper":"/paper/variance-networks-when-expectation-does-not","title":"Variance Networks: When Expectation Does Not Meet Your Expectations","date":"2018-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"da-molchanov/variance-networks","path":"variance-networks-tf/nets/utils.py","file_url":"https://github.com/da-molchanov/variance-networks/blob/HEAD/variance-networks-tf/nets/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":"3345ba65ea3269ff","mcp_get_code":{"code_sha256":"3345ba65ea3269ff"}},{"arxiv_id":"1802.08539","paper":"/paper/computation-of-optimal-transport-and-related","title":"Computation of optimal transport and related hedging problems via penalization and neural networks","date":"2018-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stephaneckstein/transport-and-related","path":"FrechetHoeffding/Figure1.py","file_url":"https://github.com/stephaneckstein/transport-and-related/blob/HEAD/FrechetHoeffding/Figure1.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc6f3096cfcd3dd1","mcp_get_code":{"code_sha256":"cc6f3096cfcd3dd1"}},{"arxiv_id":"1802.08539","paper":"/paper/computation-of-optimal-transport-and-related","title":"Computation of optimal transport and related hedging problems via penalization and neural networks","date":"2018-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stephaneckstein/transport-and-related","path":"FrechetHoeffding/Table2.py","file_url":"https://github.com/stephaneckstein/transport-and-related/blob/HEAD/FrechetHoeffding/Table2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e732376ac85a07a3","mcp_get_code":{"code_sha256":"e732376ac85a07a3"}},{"arxiv_id":"1802.08539","paper":"/paper/computation-of-optimal-transport-and-related","title":"Computation of optimal transport and related hedging problems via penalization and neural networks","date":"2018-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stephaneckstein/transport-and-related","path":"MartingaleOT/Figure2Program.py","file_url":"https://github.com/stephaneckstein/transport-and-related/blob/HEAD/MartingaleOT/Figure2Program.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d8655afa4ef8ef3f","mcp_get_code":{"code_sha256":"d8655afa4ef8ef3f"}},{"arxiv_id":"1602.03606","paper":"/paper/variations-of-the-similarity-function-of","title":"Variations of the Similarity Function of TextRank for Automated Summarization","date":"2016-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WilliamKulp/textrank","path":"summa/commons.py","file_url":"https://github.com/WilliamKulp/textrank/blob/HEAD/summa/commons.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1be547f705c87234","mcp_get_code":{"code_sha256":"1be547f705c87234"}},{"arxiv_id":"1512.09300","paper":"/paper/autoencoding-beyond-pixels-using-a-learned","title":"Autoencoding beyond pixels using a learned similarity metric","date":"2015-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baudm/vaegan-celebs-keras","path":"vaegan/models.py","file_url":"https://github.com/baudm/vaegan-celebs-keras/blob/HEAD/vaegan/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe8bd181101fc55b","mcp_get_code":{"code_sha256":"fe8bd181101fc55b"}},{"arxiv_id":"1106.5730","paper":"/paper/hogwild-a-lock-free-approach-to-parallelizing","title":"HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent","date":"2011-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lifeomic/sparkflow","path":"sparkflow/graph_utils.py","file_url":"https://github.com/lifeomic/sparkflow/blob/HEAD/sparkflow/graph_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de19052b4807c568","mcp_get_code":{"code_sha256":"de19052b4807c568"}},{"arxiv_id":"2023.emnlp-main.1005","paper":null,"title":"arXiv:2023.emnlp-main.1005","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YichenZW/Coh-MGT-Detection","path":"preprocess/construct_graph.py","file_url":"https://github.com/YichenZW/Coh-MGT-Detection/blob/HEAD/preprocess/construct_graph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c676b2eef75bb7ea","mcp_get_code":{"code_sha256":"c676b2eef75bb7ea"}}]}