{"id":"W2766453196","doi":"10.17863/cam.48429","title":"Graph Attention Networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":947,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Computer science; Graph; Attention network; Theoretical computer science; Artificial neural network; Artificial intelligence; Machine learning","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":[0.0005931603,0.0010393,0.0005134768,0.001177466,0.0005342965,0.001059736,0.001854928,0.001359917,0.004719655],"category_scores_gemma":[0.003939722,0.0003540936,0.0008170732,0.001406202,0.0009791086,0.002926103,0.00157736,0.001771157,0.001155873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403851,"about_ca_system_score_gemma":0.000741265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007196581,"about_ca_topic_score_gemma":0.01222739,"domain_scores_codex":[0.999435,0.0001374817,0.00001833147,0.0002215918,0.0001247822,0.00006282304],"domain_scores_gemma":[0.9989291,0.0005824553,0.00009877468,0.0001509283,0.000180371,0.00005840269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001437869,0.0001285031,0.002715236,0.0004003496,0.0001584258,0.0001941052,0.0002473738,0.3991851,0.007657663,0.2209785,0.03245999,0.3357309],"study_design_scores_gemma":[0.000008565062,0.00002710914,0.0005515284,0.00002751655,0.00003225136,0.00006450163,0.00002507229,0.8171505,0.001955902,0.1704422,0.009701014,0.00001389151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02500179,0.002214788,0.9534609,0.002003784,0.0002428627,0.00008737156,0.001104432,0.002077657,0.01380643],"genre_scores_gemma":[0.7180343,0.003113938,0.2516843,0.001976829,0.0003969306,0.0002489171,0.003875188,0.000429886,0.0202397],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007196581,"threshold_uncertainty_score":0.01578879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06497125518221562,"score_gpt":0.1940901129282144,"score_spread":0.1291188577459987,"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."}}