{"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/sparse-to-tuple","entry":"sparse_to_tuple","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":22,"n_papers_ran":19,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":22,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"unverified":3},"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":"2410.14886","paper":"/paper/zero-shot-generalist-graph-anomaly-detection","title":"Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mala-lab/UNPrompt","path":"utils.py","file_url":"https://github.com/mala-lab/UNPrompt/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0300d9b136d85dfb","mcp_get_code":{"code_sha256":"0300d9b136d85dfb"}},{"arxiv_id":"2407.16863","paper":"/paper/balanced-multi-relational-graph-clustering","title":"Balanced Multi-Relational Graph Clustering","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zxlearningdeep/bmgc","path":"BMGC/module/preprocess.py","file_url":"https://github.com/zxlearningdeep/bmgc/blob/HEAD/BMGC/module/preprocess.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"2406.09870","paper":"/paper/igl-bench-establishing-the-comprehensive","title":"IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RingBDStack/IGL-Bench","path":"IGL_Bench/algorithm/DEMONet/util.py","file_url":"https://github.com/RingBDStack/IGL-Bench/blob/HEAD/IGL_Bench/algorithm/DEMONet/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"2406.02059","paper":"/paper/graph-adversarial-diffusion-convolution","title":"Graph Adversarial Diffusion Convolution","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songtaoliu0823/gadc","path":"Adaptive_Adversarial_Attack/utils.py","file_url":"https://github.com/songtaoliu0823/gadc/blob/HEAD/Adaptive_Adversarial_Attack/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"2310.11676","paper":"/paper/prem-a-simple-yet-effective-approach-for-node","title":"PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection","date":"2023-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"campanulabells/prem-gad","path":"modules/utils.py","file_url":"https://github.com/campanulabells/prem-gad/blob/HEAD/modules/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0300d9b136d85dfb","mcp_get_code":{"code_sha256":"0300d9b136d85dfb"}},{"arxiv_id":"2310.01892","paper":"/paper/figure-simple-and-efficient-unsupervised-node-1","title":"FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/figure","path":"utils.py","file_url":"https://github.com/microsoft/figure/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"2310.00800","paper":null,"title":"arXiv:2310.00800","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"jumxglhf/ParetoGNN","path":"link_gen.py","file_url":"https://github.com/jumxglhf/ParetoGNN/blob/HEAD/link_gen.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"78ccfe0f0f11ce2c","mcp_get_code":{"code_sha256":"78ccfe0f0f11ce2c"}},{"arxiv_id":"2302.02941","paper":"/paper/on-over-squashing-in-message-passing-neural","title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology","date":"2023-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lrnzgiusti/on-oversquashing","path":"utils/utils.py","file_url":"https://github.com/lrnzgiusti/on-oversquashing/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"2203.03315","paper":"/paper/deep-reinforcement-learning-for-entity-1","title":"Deep Reinforcement Learning for Entity Alignment","date":"2022-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guolingbing/RLEA","path":"src/openea/approaches/alinet.py","file_url":"https://github.com/guolingbing/RLEA/blob/HEAD/src/openea/approaches/alinet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"05d5e3c8eda73847","mcp_get_code":{"code_sha256":"05d5e3c8eda73847"}},{"arxiv_id":"2106.06935","paper":"/paper/neural-bellman-ford-networks-a-general-graph","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fs302/EasyLink","path":"easylink/common/data_utils.py","file_url":"https://github.com/fs302/EasyLink/blob/HEAD/easylink/common/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":"4ada13b9756aa281","mcp_get_code":{"code_sha256":"4ada13b9756aa281"}},{"arxiv_id":"2104.14210","paper":"/paper/biased-edge-dropout-for-enhancing-fairness-in","title":"FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning","date":"2021-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juellsprott/graphair-reproducibility","path":"models/fairgraph/method/fairadj/utils.py","file_url":"https://github.com/juellsprott/graphair-reproducibility/blob/HEAD/models/fairgraph/method/fairadj/utils.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"2002.06755","paper":"/paper/unifying-graph-convolutional-neural-networks-1","title":"Unifying Graph Convolutional Neural Networks and Label Propagation","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"achalagarwal/gcn-lpa","path":"src/data_loader.py","file_url":"https://github.com/achalagarwal/gcn-lpa/blob/HEAD/src/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13c5950f6e3ab34b","mcp_get_code":{"code_sha256":"13c5950f6e3ab34b"}},{"arxiv_id":"2002.03665","paper":"/paper/anomalydae-dual-autoencoder-for-anomaly","title":"AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks","date":"2020-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoyfan/AnomalyDAE","path":"src/preprocessing.py","file_url":"https://github.com/haoyfan/AnomalyDAE/blob/HEAD/src/preprocessing.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"1908.07078","paper":"/paper/semi-implicit-graph-variational-auto-encoders","title":"Semi-Implicit Graph Variational Auto-Encoders","date":"2019-08-19","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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"1906.05017","paper":"/paper/graph-embedding-on-biomedical-networks","title":"Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangyue9607/BioNEV","path":"src/bionev/GAE/preprocessing.py","file_url":"https://github.com/xiangyue9607/BioNEV/blob/HEAD/src/bionev/GAE/preprocessing.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"1904.00326","paper":"/paper/medgcn-graph-convolutional-networks-for","title":"MedGCN: Medication recommendation and lab test imputation via graph convolutional networks","date":"2019-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mocherson/MedGCN","path":"utility/preprocessing.py","file_url":"https://github.com/mocherson/MedGCN/blob/HEAD/utility/preprocessing.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"1903.04154","paper":"/paper/fisher-bures-adversary-graph-convolutional","title":"Fisher-Bures Adversary Graph Convolutional Networks","date":"2019-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stellargraph/FisherGCN","path":"gcn/utils.py","file_url":"https://github.com/stellargraph/FisherGCN/blob/HEAD/gcn/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"1804.00099","paper":"/paper/graph-convolutional-neural-networks-via-1","title":"Graph Convolutional Neural Networks via Scattering","date":"2018-03-31","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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"1611.07308","paper":"/paper/variational-graph-auto-encoders","title":"Variational Graph Auto-Encoders","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Omairss/RepresentationLearning","path":"src/preprocessing.py","file_url":"https://github.com/Omairss/RepresentationLearning/blob/HEAD/src/preprocessing.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"ijcai2022_0498","paper":null,"title":"arXiv:ijcai2022_0498","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xinxingwu-uk/DGAE","path":"Python/Models/dgae_alpha/preprocessing.py","file_url":"https://github.com/xinxingwu-uk/DGAE/blob/HEAD/Python/Models/dgae_alpha/preprocessing.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":"3965c8f8e2d0023e","mcp_get_code":{"code_sha256":"3965c8f8e2d0023e"}},{"arxiv_id":"ijcai2022_0310","paper":null,"title":"arXiv:ijcai2022_0310","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zjunet/PathNet","path":"dataset.py","file_url":"https://github.com/zjunet/PathNet/blob/HEAD/dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}},{"arxiv_id":"aaai_20388","paper":null,"title":"arXiv:aaai_20388","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xuehansheng/RepBin","path":"utils.py","file_url":"https://github.com/xuehansheng/RepBin/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaa82e9c7a22e930","mcp_get_code":{"code_sha256":"eaa82e9c7a22e930"}}]}