{"id":"W3190849533","doi":"10.1007/s13042-021-01400-x","title":"Weakly-supervised learning for community detection based on graph convolution in attributed networks","year":2021,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Computer science; Graph; Artificial intelligence; Feature learning; Semi-supervised learning; Community structure; Machine learning; Theoretical computer science; Data mining; Mathematics","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":[0.0007256685,0.0001177869,0.000217452,0.0001896868,0.0001419781,0.0000775515,0.0001433586,0.00004764884,0.0000615094],"category_scores_gemma":[0.0001318524,0.0001191509,0.0001517203,0.0001683629,0.00002927532,0.00005432329,0.00004756204,0.001032822,4.240048e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005297138,"about_ca_system_score_gemma":0.00003127406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001979036,"about_ca_topic_score_gemma":0.00009456777,"domain_scores_codex":[0.998709,0.0004692602,0.0003702283,0.000102756,0.0002174255,0.000131289],"domain_scores_gemma":[0.9986354,0.0004905207,0.0003014681,0.00006701038,0.0004570016,0.00004866247],"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":[0.0002672822,0.0002997905,0.3343394,0.00000672837,0.0002176756,0.00000895098,0.000151831,0.5391678,0.001528052,0.0005722002,0.00005409501,0.1233863],"study_design_scores_gemma":[0.001475131,0.0003069106,0.02276313,0.0001357965,0.00005751483,0.00001030819,0.0002168271,0.9671777,0.001210038,0.001955261,0.004551591,0.0001397661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5456611,0.0002281573,0.4532132,0.0002966109,0.0001604557,0.00006065819,0.000004396851,0.00002017542,0.0003552026],"genre_scores_gemma":[0.9981133,0.00005679513,0.001344459,0.00005327539,0.0002196977,0.000004173319,0.000104749,0.00001433843,0.00008924337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4524522,"threshold_uncertainty_score":0.4858834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178961823301724,"score_gpt":0.2739264792811901,"score_spread":0.2621368610481729,"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."}}