{"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/calculate-normalized-laplacian","entry":"calculate_normalized_laplacian","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":11,"n_papers_ran":9,"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":6,"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":2,"ran_fixture":0,"ran":2,"unverified":2},"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":"2602.05286","paper":"/paper/arxiv-2602-05286","title":"HealthMamba: An Uncertainty-aware Spatiotemporal Graph State Space Model for Effective and Reliable Healthcare Facility Visit Prediction","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"UFOdestiny/HealthMamba","path":"utils/graph_algo.py","file_url":"https://github.com/UFOdestiny/HealthMamba/blob/HEAD/utils/graph_algo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a616b4dba35c9611","mcp_get_code":{"code_sha256":"a616b4dba35c9611"}},{"arxiv_id":"2410.00373","paper":"/paper/robust-traffic-forecasting-against-spatial","title":"Robust Traffic Forecasting against Spatial Shift over Years","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dreamzz5/st-expert","path":"src/utils/graph_algo.py","file_url":"https://github.com/dreamzz5/st-expert/blob/HEAD/src/utils/graph_algo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"52c34de0cec03ed2","mcp_get_code":{"code_sha256":"52c34de0cec03ed2"}},{"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/util.py","file_url":"https://github.com/AnonymousSAUC/SAUC/blob/HEAD/models/GWN/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"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":"util.py","file_url":"https://github.com/HyunWookL/TESTAM/blob/HEAD/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"arxiv_id":"2312.11198","paper":"/paper/signed-graph-neural-ordinary-differential","title":"Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics","date":"2023-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beautyonce/sgode","path":"SGODE-RNN/lib/utils.py","file_url":"https://github.com/beautyonce/sgode/blob/HEAD/SGODE-RNN/lib/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"arxiv_id":"2310.10196","paper":"/paper/large-models-for-time-series-and-spatio","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenhaomin/DiffSTG","path":"algorithm/diffstg/graph_algo.py","file_url":"https://github.com/wenhaomin/DiffSTG/blob/HEAD/algorithm/diffstg/graph_algo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ceffc7d0f53c1ae","mcp_get_code":{"code_sha256":"1ceffc7d0f53c1ae"}},{"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/lib/utils.py","file_url":"https://github.com/nhuang37/approx_equivariant_graph_nets/blob/HEAD/DCRNN_Pytorch/lib/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"arxiv_id":"2211.11176","paper":"/paper/spatiotemporal-modeling-of-multivariate","title":"Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models","date":"2022-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsy935/graphs4mer","path":"model/graphs4mer.py","file_url":"https://github.com/tsy935/graphs4mer/blob/HEAD/model/graphs4mer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aab13fce6f38406c","mcp_get_code":{"code_sha256":"aab13fce6f38406c"}},{"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":"sshleifer/Graph-WaveNet","path":"util.py","file_url":"https://github.com/sshleifer/Graph-WaveNet/blob/HEAD/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"e7b2e6a60daa42ca","mcp_get_code":{"code_sha256":"e7b2e6a60daa42ca"}},{"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":"KimMeen/DCRNN","path":"xlwang_version/dcrnn_model.py","file_url":"https://github.com/KimMeen/DCRNN/blob/HEAD/xlwang_version/dcrnn_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f342093fe5f43a5","mcp_get_code":{"code_sha256":"2f342093fe5f43a5"}},{"arxiv_id":"ijcai2025_0372","paper":null,"title":"arXiv:ijcai2025_0372","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cwang-nus/DOL","path":"utils/graph_algo.py","file_url":"https://github.com/cwang-nus/DOL/blob/HEAD/utils/graph_algo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ceffc7d0f53c1ae","mcp_get_code":{"code_sha256":"1ceffc7d0f53c1ae"}}]}