{"id":"W2963782635","doi":"10.17863/cam.40744","title":"Deep Graph Infomax","year":2018,"lang":"en","type":"article","venue":"Apollo (University of Cambridge)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":332,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"European Commission","keywords":"Infomax; Computer science; Graph; Artificial intelligence; Unsupervised learning; Node (physics); Machine learning; Competitive learning; Feature learning; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008044065,0.0001136067,0.0001655972,0.000176318,0.0002223118,0.0000176838,0.001082178,0.0000649166,0.00001999597],"category_scores_gemma":[0.000009924687,0.0001396278,0.000117959,0.0008358596,0.0003538836,0.0007690304,0.0003777371,0.0001082004,0.0001049521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002030007,"about_ca_system_score_gemma":0.00002876399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001234585,"about_ca_topic_score_gemma":0.0001038034,"domain_scores_codex":[0.999079,0.000034435,0.00009147411,0.0002964449,0.0002233129,0.0002752614],"domain_scores_gemma":[0.9990119,0.0000494685,0.000117594,0.0005648716,0.0001367991,0.0001193191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001398992,0.0001794688,0.007804719,0.000043259,0.0001861899,0.0002436717,0.003360854,0.0004668763,0.004503023,0.7513056,0.0384293,0.1933371],"study_design_scores_gemma":[0.008340055,0.003078154,0.342762,0.0002556915,0.0001963015,0.0003187968,0.002696672,0.3437509,0.01211974,0.05162951,0.2311527,0.003699483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09580417,0.00007230847,0.8915676,0.0008272559,0.0003331904,0.00009517803,0.000001847085,0.0001740056,0.01112446],"genre_scores_gemma":[0.9478074,0.00003553079,0.05057031,0.0002590503,0.00005891996,8.413227e-8,0.000001710196,0.000006267819,0.001260729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8520032,"threshold_uncertainty_score":0.5693854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007093247053127333,"score_gpt":0.1900923582138747,"score_spread":0.1829991111607473,"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."}}