{"id":"W4382203390","doi":"10.1609/aaai.v37i8.26168","title":"Neighbor Contrastive Learning on Learnable Graph Augmentation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Hainan University; National Natural Science Foundation of China","keywords":"Computer science; Graph; Artificial intelligence; False positive paradox; Theoretical computer science; 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":[0.001410386,0.001975336,0.001730552,0.001425509,0.0006175961,0.001013574,0.003444316,0.001954165,0.003207454],"category_scores_gemma":[0.005597925,0.0007384655,0.001161998,0.00114429,0.001718691,0.00353374,0.002844005,0.003097315,0.001469564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224064,"about_ca_system_score_gemma":0.0007755462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00240826,"about_ca_topic_score_gemma":0.004261024,"domain_scores_codex":[0.9988542,0.0003387084,0.00003348551,0.0004639754,0.0002063566,0.0001033086],"domain_scores_gemma":[0.9972569,0.001229469,0.0002788662,0.0007363798,0.0003471181,0.0001511849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004211552,0.000407553,0.002417608,0.0003017133,0.0001149949,0.0002735388,0.0002998686,0.3942703,0.01598317,0.02402918,0.01408521,0.5473956],"study_design_scores_gemma":[0.00001775185,0.00006708685,0.0001671127,0.000009960146,0.00000973016,0.00005229041,0.00001721472,0.9841485,0.00262932,0.01189173,0.0009795536,0.000009854382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03383708,0.0004568931,0.9585601,0.0003266614,0.00008191885,0.0001136496,0.0002861497,0.00445687,0.001880678],"genre_scores_gemma":[0.5506953,0.0003635327,0.4370157,0.0009404516,0.0001790667,0.0003969477,0.002409258,0.0008162482,0.007183409],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003444316,"threshold_uncertainty_score":0.01072997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06447864412558238,"score_gpt":0.2998504342094437,"score_spread":0.2353717900838614,"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."}}