{"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/generate-graph-seq2seq-io-data","entry":"generate_graph_seq2seq_io_data","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":10,"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":8,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":8},"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":"2409.08766","paper":"/paper/sauc-sparsity-aware-uncertainty-calibration","title":"SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks","date":"2024-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AnonymousSAUC/SAUC","path":"models/GWN/generate_training_data.py","file_url":"https://github.com/AnonymousSAUC/SAUC/blob/HEAD/models/GWN/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2635b8fbfe12d9ff","mcp_get_code":{"code_sha256":"2635b8fbfe12d9ff"}},{"arxiv_id":"2403.02600","paper":"/paper/testam-a-time-enhanced-spatio-temporal","title":"TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HyunWookL/TESTAM","path":"generate_training_data.py","file_url":"https://github.com/HyunWookL/TESTAM/blob/HEAD/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd669f99f4fe7e0f","mcp_get_code":{"code_sha256":"dd669f99f4fe7e0f"}},{"arxiv_id":"2308.10436","paper":"/paper/approximately-equivariant-graph-networks-1","title":"Approximately Equivariant Graph Networks","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nhuang37/approx_equivariant_graph_nets","path":"DCRNN_Pytorch/generate_training_data_t3.py","file_url":"https://github.com/nhuang37/approx_equivariant_graph_nets/blob/HEAD/DCRNN_Pytorch/generate_training_data_t3.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fde1716c2510a3a6","mcp_get_code":{"code_sha256":"fde1716c2510a3a6"}},{"arxiv_id":"2307.08390","paper":"/paper/correlation-aware-spatial-temporal-graph","title":"Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huankoh/cst-gl","path":"generate_data/generate_training_data.py","file_url":"https://github.com/huankoh/cst-gl/blob/HEAD/generate_data/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d3064a8936992a9","mcp_get_code":{"code_sha256":"8d3064a8936992a9"}},{"arxiv_id":"2211.14701","paper":"/paper/spatio-temporal-meta-graph-learning-for","title":"Spatio-Temporal Meta-Graph Learning for Traffic Forecasting","date":"2022-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fde1716c2510a3a6","mcp_get_code":{"code_sha256":"fde1716c2510a3a6"}},{"arxiv_id":"2101.06861","paper":"/paper/discrete-graph-structure-learning-for-1","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","date":"2021-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaoshangcs/gts","path":"scripts/generate_training_data.py","file_url":"https://github.com/chaoshangcs/gts/blob/HEAD/scripts/generate_training_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":"73ab52a64a470ca4","mcp_get_code":{"code_sha256":"73ab52a64a470ca4"}},{"arxiv_id":"2007.15531","paper":"/paper/fc-gaga-fully-connected-gated-graph","title":"FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting","date":"2020-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boreshkinai/fc-gaga","path":"generate_training_data.py","file_url":"https://github.com/boreshkinai/fc-gaga/blob/HEAD/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fffbee9635716611","mcp_get_code":{"code_sha256":"fffbee9635716611"}},{"arxiv_id":"1906.00121","paper":"/paper/190600121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiahuiSun/Exp-Graph-WaveNet","path":"generate_training_data.py","file_url":"https://github.com/JiahuiSun/Exp-Graph-WaveNet/blob/HEAD/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc148af599e95656","mcp_get_code":{"code_sha256":"fc148af599e95656"}},{"arxiv_id":"1906.00121","paper":"/paper/190600121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simonvino/graphwavenet_brain_connectivity","path":"old_stuff/generate_training_data.py","file_url":"https://github.com/simonvino/graphwavenet_brain_connectivity/blob/HEAD/old_stuff/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1175f39f2d567546","mcp_get_code":{"code_sha256":"1175f39f2d567546"}},{"arxiv_id":"1707.01926","paper":"/paper/diffusion-convolutional-recurrent-neural","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting","date":"2017-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chnsh/DCRNN","path":"scripts/generate_training_data.py","file_url":"https://github.com/chnsh/DCRNN/blob/HEAD/scripts/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fde1716c2510a3a6","mcp_get_code":{"code_sha256":"fde1716c2510a3a6"}},{"arxiv_id":"aaai_25976","paper":null,"title":"arXiv:aaai_25976","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"deepkashiwa20/MegaCRN","path":"generate_training_data.py","file_url":"https://github.com/deepkashiwa20/MegaCRN/blob/HEAD/generate_training_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fde1716c2510a3a6","mcp_get_code":{"code_sha256":"fde1716c2510a3a6"}}]}