{"id":"W3091261939","doi":"10.48550/arxiv.2010.02089","title":"CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"ENCODE; Computer science; Leverage (statistics); Theoretical computer science; Graph; Correlation; Artificial neural network; Copula (linguistics); Node (physics); Artificial intelligence; Machine learning; Data mining; Mathematics; Econometrics","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":[0.002906124,0.001308282,0.001344819,0.001597966,0.0006302819,0.001583259,0.002261437,0.001520416,0.001563378],"category_scores_gemma":[0.01392684,0.0008741432,0.001117164,0.002512933,0.001411619,0.004375339,0.002227968,0.003035748,0.0005741037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001704365,"about_ca_system_score_gemma":0.001683064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01100503,"about_ca_topic_score_gemma":0.01568741,"domain_scores_codex":[0.9988521,0.0005932177,0.00004244355,0.0002893528,0.000141725,0.00008123235],"domain_scores_gemma":[0.9946622,0.003444535,0.0004446544,0.000603318,0.0006556798,0.0001896197],"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":[0.0001002913,0.00009530174,0.003316091,0.0001809496,0.0001533457,0.000129608,0.0001759829,0.8119843,0.001235877,0.07496396,0.004534571,0.1031298],"study_design_scores_gemma":[0.000002483446,0.000009001777,0.0001019524,0.000007514898,0.000007941767,0.00001145793,0.000006529543,0.9729962,0.0001254037,0.02639646,0.0003305685,0.000004497325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01320374,0.0005527977,0.9840422,0.0005731188,0.00005060007,0.00004401286,0.0001618794,0.0004043691,0.0009674052],"genre_scores_gemma":[0.5209848,0.001911319,0.4703321,0.0009962198,0.000186954,0.0002895918,0.0009665045,0.0003992222,0.003933293],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01100503,"threshold_uncertainty_score":0.021882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05592077022521464,"score_gpt":0.2100263340923151,"score_spread":0.1541055638671004,"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."}}