{"id":"W4389778193","doi":"10.1016/j.patcog.2023.110209","title":"A contrastive variational graph auto-encoder for node clustering","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Randomness; Computer science; Feature (linguistics); Upper and lower bounds; Inference; Algorithm; Encoder; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009823845,0.0006735065,0.001253907,0.0008498398,0.0004930398,0.000897533,0.002903778,0.001979681,0.004013105],"category_scores_gemma":[0.003293518,0.0007998972,0.0008551588,0.00102762,0.0008008303,0.001691544,0.001721769,0.002239617,0.001743747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185746,"about_ca_system_score_gemma":0.001375708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108577,"about_ca_topic_score_gemma":0.02470328,"domain_scores_codex":[0.9995205,0.0001461571,0.00002014865,0.0001518961,0.0001122517,0.00004902694],"domain_scores_gemma":[0.9988527,0.0005872587,0.00004808965,0.0002202082,0.00021866,0.00007304717],"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":[0.0002389207,0.0001398782,0.0006578288,0.000168547,0.0001316876,0.0001079311,0.0001210313,0.5788983,0.01051195,0.08954604,0.01264021,0.3068376],"study_design_scores_gemma":[0.000004996971,0.000008631439,0.00003808428,0.000004664583,0.000005267725,0.00001149209,0.000003897201,0.9895529,0.0005900225,0.009300918,0.0004750401,0.000004101218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007492784,0.0002764145,0.989526,0.000191162,0.00007700639,0.00004146892,0.0002764617,0.0009813718,0.001137184],"genre_scores_gemma":[0.321276,0.0003869213,0.6646183,0.0004764473,0.0001387709,0.0002172356,0.001576993,0.0008690434,0.01044029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0108577,"threshold_uncertainty_score":0.02158898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0409770510361664,"score_gpt":0.2727985179681225,"score_spread":0.2318214669319562,"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."}}