{"id":"W4386727921","doi":"10.20944/preprints202309.0962.v1","title":"Directional Graph Attention Networks","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Attention network; Data mining; Margin (machine learning); Theoretical computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007561856,0.001108561,0.0006692771,0.001606483,0.000416398,0.0009071394,0.001216628,0.001052597,0.002914799],"category_scores_gemma":[0.003889194,0.0004012899,0.0009219025,0.001898683,0.0007098551,0.002044198,0.001085719,0.001159955,0.000684685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001606651,"about_ca_system_score_gemma":0.0006259913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082928,"about_ca_topic_score_gemma":0.01238128,"domain_scores_codex":[0.9994873,0.00013478,0.00002079227,0.0002081489,0.00009467069,0.00005422464],"domain_scores_gemma":[0.998772,0.0006653822,0.0001352168,0.0001142632,0.0002577583,0.00005531471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000211393,0.0001190387,0.005769021,0.0004507705,0.0002109159,0.0001857536,0.0002144921,0.5636006,0.005476643,0.09224866,0.0157899,0.3157227],"study_design_scores_gemma":[0.000008425284,0.00003677339,0.001117443,0.00002169717,0.00004460704,0.00006365208,0.00002413354,0.9407242,0.000848571,0.05195499,0.005142796,0.00001276718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0727976,0.006235905,0.9005253,0.002191902,0.0003549574,0.0001394062,0.00135725,0.001622128,0.01477563],"genre_scores_gemma":[0.85732,0.005767826,0.1160381,0.001257972,0.0004831417,0.0002366994,0.002864689,0.0002537206,0.01577795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01082928,"threshold_uncertainty_score":0.02153248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264386529009888,"score_gpt":0.3404159394477672,"score_spread":0.2139772865467784,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}