{"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-mx-to-torch-sparse-tensor","entry":"sparse_mx_to_torch_sparse_tensor","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":41,"n_papers_ran":33,"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":13,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":42,"n_places_pointer_only":19,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"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":"2602.05358","paper":"/paper/arxiv-2602-05358","title":"Bayesian Neighborhood Adaptation for Graph Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"paribeshregmi/BNA-GNN","path":"utils/utils.py","file_url":"https://github.com/paribeshregmi/BNA-GNN/blob/HEAD/utils/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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2601.17469","paper":"/paper/arxiv-2601-17469","title":"Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","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":"f18d89df1cb06ebb","mcp_get_code":{"code_sha256":"f18d89df1cb06ebb"}},{"arxiv_id":"2510.22643","paper":"/paper/arxiv-2510-22643","title":"Enhancing Graph Classification Robustness with Singular Pooling","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"king/rs-pool","path":"src/models/utils.py","file_url":"https://github.com/king/rs-pool/blob/HEAD/src/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"208d1e7dd9c65347","mcp_get_code":{"code_sha256":"208d1e7dd9c65347"}},{"arxiv_id":"2502.19670","paper":"/paper/training-robust-graph-neural-networks-by","title":"Training Robust Graph Neural Networks by Modeling Noise Dependencies","date":"2025-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yeonjun-in/torch-da-gnn","path":"models/dagnn.py","file_url":"https://github.com/yeonjun-in/torch-da-gnn/blob/HEAD/models/dagnn.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":"f18d89df1cb06ebb","mcp_get_code":{"code_sha256":"f18d89df1cb06ebb"}},{"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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2407.14459","paper":"/paper/polyformer-scalable-node-wise-filters-via","title":"PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"air029/polyformer","path":"node_classification_large_graph/processing_dataset.py","file_url":"https://github.com/air029/polyformer/blob/HEAD/node_classification_large_graph/processing_dataset.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2407.11624","paper":"/paper/rethinking-fair-graph-neural-networks-from-re","title":"Rethinking Fair Graph Neural Networks from Re-balancing","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhixunlee/fairgb","path":"data_utils.py","file_url":"https://github.com/zhixunlee/fairgb/blob/HEAD/data_utils.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":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2405.16771","paper":"/paper/arc-a-generalist-graph-anomaly-detector-with","title":"ARC: A Generalist Graph Anomaly Detector with In-Context Learning","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yixinliu233/arc","path":"utils.py","file_url":"https://github.com/yixinliu233/arc/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2405.03342","paper":"/paper/doubly-robust-causal-effect-estimation-under","title":"Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DMIRLAB-Group/TNet","path":"src/utils.py","file_url":"https://github.com/DMIRLAB-Group/TNet/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fdb4158a7c629d1","mcp_get_code":{"code_sha256":"8fdb4158a7c629d1"}},{"arxiv_id":"2404.00658","paper":"/paper/ktpformer-kinematics-and-trajectory-prior","title":"KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation","date":"2024-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JihuaPeng/KTPFormer","path":"common/graph_utils.py","file_url":"https://github.com/JihuaPeng/KTPFormer/blob/HEAD/common/graph_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2403.07954","paper":"/paper/optimizing-polynomial-graph-filters-a-novel","title":"Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kkhuang81/AdaptKry","path":"m3utils.py","file_url":"https://github.com/kkhuang81/AdaptKry/blob/HEAD/m3utils.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2402.11518","paper":"/paper/large-language-model-driven-meta-structure","title":"Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linchen-65/restruct","path":"preprocess_node_classification.py","file_url":"https://github.com/linchen-65/restruct/blob/HEAD/preprocess_node_classification.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9378be67198d5141","mcp_get_code":{"code_sha256":"9378be67198d5141"}},{"arxiv_id":"2402.06087","paper":"/paper/descriptive-kernel-convolution-network-with","title":"Descriptive Kernel Convolution Network with Improved Random Walk Kernel","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mengchillee/rwk_plus","path":"sec_5_1_RWKP/anomaly_detection/utlis.py","file_url":"https://github.com/mengchillee/rwk_plus/blob/HEAD/sec_5_1_RWKP/anomaly_detection/utlis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8173512105bae4fb","mcp_get_code":{"code_sha256":"8173512105bae4fb"}},{"arxiv_id":"2401.11760","paper":"/paper/towards-effective-and-general-graph","title":"Towards Effective and General Graph Unlearning via Mutual Evolution","date":"2024-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xkli-allen/megu","path":"exp/exp_megu.py","file_url":"https://github.com/xkli-allen/megu/blob/HEAD/exp/exp_megu.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2401.11755","paper":"/paper/fedgta-topology-aware-averaging-for-federated","title":"FedGTA: Topology-aware Averaging for Federated Graph Learning","date":"2024-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xkli-allen/fedgta","path":"utils.py","file_url":"https://github.com/xkli-allen/fedgta/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2312.11109","paper":"/paper/graph-transformers-for-large-graphs","title":"Graph Transformers for Large Graphs","date":"2023-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-research/largegt","path":"data_utils.py","file_url":"https://github.com/snap-research/largegt/blob/HEAD/data_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2312.08697","paper":"/paper/incomplete-contrastive-multi-view-clustering","title":"Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liunian-jay/icmvc","path":"utils/graph_adjacency.py","file_url":"https://github.com/liunian-jay/icmvc/blob/HEAD/utils/graph_adjacency.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2310.02861","paper":"/paper/rayleigh-quotient-graph-neural-networks-for","title":"Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xydong127/rqgnn","path":"utils.py","file_url":"https://github.com/xydong127/rqgnn/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3a4f32143a136d50","mcp_get_code":{"code_sha256":"3a4f32143a136d50"}},{"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":"Human_Pose_Est/common/graph_utils.py","file_url":"https://github.com/nhuang37/approx_equivariant_graph_nets/blob/HEAD/Human_Pose_Est/common/graph_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"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_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2306.08274","paper":"/paper/why-using-either-aggregated-features-or","title":"A Simple and Scalable Graph Neural Network for Large Directed Graphs","date":"2023-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seijimaekawa/a2dug","path":"src/process_spmm_dire.py","file_url":"https://github.com/seijimaekawa/a2dug/blob/HEAD/src/process_spmm_dire.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2305.18457","paper":"/paper/learning-strong-graph-neural-networks-with","title":"Learning Strong Graph Neural Networks with Weak Information","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yixinliu233/d2pt","path":"utils.py","file_url":"https://github.com/yixinliu233/d2pt/blob/HEAD/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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2302.02410","paper":"/paper/see-you-soon-decoupled-iterative-refinement","title":"Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image","date":"2023-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PengfeiRen96/DIR","path":"SemGCN/utils.py","file_url":"https://github.com/PengfeiRen96/DIR/blob/HEAD/SemGCN/utils.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":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2211.16940","paper":"/paper/diffpose-toward-more-reliable-3d-pose","title":"DiffPose: Toward More Reliable 3D Pose Estimation","date":"2022-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GONGJIA0208/Diffpose","path":"models/ChebConv.py","file_url":"https://github.com/GONGJIA0208/Diffpose/blob/HEAD/models/ChebConv.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":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2210.07606","paper":"/paper/revisiting-heterophily-for-graph-neural","title":"Revisiting Heterophily For Graph Neural Networks","date":"2022-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SitaoLuan/ACM-GNN","path":"ACM-Geometric/utils.py","file_url":"https://github.com/SitaoLuan/ACM-GNN/blob/HEAD/ACM-Geometric/utils.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":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2209.00891","paper":"/paper/multi-modal-contrastive-representation","title":"Multi-modal Contrastive Representation Learning for Entity Alignment","date":"2022-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzxlin/mclea","path":"src/utils.py","file_url":"https://github.com/lzxlin/mclea/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":"edf110cbd4fe5ec5","mcp_get_code":{"code_sha256":"edf110cbd4fe5ec5"}},{"arxiv_id":"2206.01506","paper":"/paper/can-hybrid-geometric-scattering-networks-help","title":"Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?","date":"2022-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yimengmin/geometricscatteringmaximalclique","path":"models.py","file_url":"https://github.com/yimengmin/geometricscatteringmaximalclique/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdc1f1dad6d978e7","mcp_get_code":{"code_sha256":"bdc1f1dad6d978e7"}},{"arxiv_id":"2205.13459","paper":"/paper/sigmanet-one-laplacian-to-rule-them-all","title":"SigMaNet: One Laplacian to Rule Them All","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stefa1994/sigmanet","path":"src/Edge_SigMaNet.py","file_url":"https://github.com/stefa1994/sigmanet/blob/HEAD/src/Edge_SigMaNet.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":"e0edf5cb2f7c039f","mcp_get_code":{"code_sha256":"e0edf5cb2f7c039f"}},{"arxiv_id":"2203.16863","paper":"/paper/cross-domain-recommendation-to-cold-start","title":"Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjx96/cdrib","path":"CDRIB/utils/GraphMaker.py","file_url":"https://github.com/cjx96/cdrib/blob/HEAD/CDRIB/utils/GraphMaker.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2110.14038","paper":"/paper/robustness-of-graph-neural-networks-at-scale","title":"Robustness of Graph Neural Networks at Scale","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sigeisler/robustness_of_gnns_at_scale","path":"rgnn_at_scale/models/deeprobust_rgcn.py","file_url":"https://github.com/sigeisler/robustness_of_gnns_at_scale/blob/HEAD/rgnn_at_scale/models/deeprobust_rgcn.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2107.03573","paper":"/paper/deep-structural-point-process-for-learning","title":"Deep Structural Point Process for Learning Temporal Interaction Networks","date":"2021-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjx96/DSPP","path":"DSPP_src/utils/BipartiteGraph.py","file_url":"https://github.com/cjx96/DSPP/blob/HEAD/DSPP_src/utils/BipartiteGraph.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef85e6c6e0154e09","mcp_get_code":{"code_sha256":"ef85e6c6e0154e09"}},{"arxiv_id":"2103.17138","paper":"/paper/soon-scenario-oriented-object-navigation-with","title":"SOON: Scenario Oriented Object Navigation with Graph-based Exploration","date":"2021-03-31","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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2102.11391","paper":"/paper/magnet-a-magnetic-neural-network-for-directed","title":"MagNet: A Neural Network for Directed Graphs","date":"2021-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matthew-hirn/magnet","path":"src/sparse_Magnet.py","file_url":"https://github.com/matthew-hirn/magnet/blob/HEAD/src/sparse_Magnet.py","status":"unverified","verification_level":0,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"faa54fc62495a40c","mcp_get_code":{"code_sha256":"faa54fc62495a40c"}},{"arxiv_id":"2009.13603","paper":"/paper/visual-pivoting-for-unsupervised-entity","title":"Visual Pivoting for (Unsupervised) Entity Alignment","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridgeltl/eva","path":"src/utils.py","file_url":"https://github.com/cambridgeltl/eva/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":"edf110cbd4fe5ec5","mcp_get_code":{"code_sha256":"edf110cbd4fe5ec5"}},{"arxiv_id":"2008.05089","paper":"/paper/quaternion-graph-neural-networks","title":"Quaternion Graph Neural Networks","date":"2020-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daiquocnguyen/QGNN","path":"QGNN_pytorch/train_node_cls.py","file_url":"https://github.com/daiquocnguyen/QGNN/blob/HEAD/QGNN_pytorch/train_node_cls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"56364513ade08760","mcp_get_code":{"code_sha256":"56364513ade08760"}},{"arxiv_id":"2004.01899","paper":"/paper/a-generic-graph-based-neural-architecture","title":"A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS","date":"2020-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"walkerning/aw_nas","path":"aw_nas/evaluator/arch_network.py","file_url":"https://github.com/walkerning/aw_nas/blob/HEAD/aw_nas/evaluator/arch_network.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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"2002.01633","paper":"/paper/structural-deep-clustering-network","title":"Structural Deep Clustering Network","date":"2020-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"461054993/SDCN","path":"utils.py","file_url":"https://github.com/461054993/SDCN/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"1902.07153","paper":"/paper/simplifying-graph-convolutional-networks","title":"Simplifying Graph Convolutional Networks","date":"2019-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tiiiger/SGC","path":"utils.py","file_url":"https://github.com/Tiiiger/SGC/blob/HEAD/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":"c97b99c4e8201a97","mcp_get_code":{"code_sha256":"c97b99c4e8201a97"}},{"arxiv_id":"aaai_29088","paper":null,"title":"arXiv:aaai_29088","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Stefa1994/QuaterGCN","path":"src/QuaterGCN.py","file_url":"https://github.com/Stefa1994/QuaterGCN/blob/HEAD/src/QuaterGCN.py","status":"unverified","verification_level":0,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"faa54fc62495a40c","mcp_get_code":{"code_sha256":"faa54fc62495a40c"}},{"arxiv_id":"aaai_29088","paper":null,"title":"arXiv:aaai_29088","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Stefa1994/QuaterGCN","path":"src/Edge_QuaterGCN.py","file_url":"https://github.com/Stefa1994/QuaterGCN/blob/HEAD/src/Edge_QuaterGCN.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":"e0edf5cb2f7c039f","mcp_get_code":{"code_sha256":"e0edf5cb2f7c039f"}},{"arxiv_id":"aaai_26026","paper":null,"title":"arXiv:aaai_26026","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"JHL-HUST/PMMM","path":"node_classification/preprocess.py","file_url":"https://github.com/JHL-HUST/PMMM/blob/HEAD/node_classification/preprocess.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9378be67198d5141","mcp_get_code":{"code_sha256":"9378be67198d5141"}}]}