{"id":"W3139834472","doi":"10.2991/ijcis.d.210329.001","title":"Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making","year":2021,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Ministry of Education, India; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Centrality; Cohesion (chemistry); Computer science; Node (physics); Group decision-making; Construct (python library); Function (biology); Network analysis; Stability (learning theory); Group cohesiveness; Scale (ratio); Data mining; Algorithm; Theoretical computer science; Mathematics; Statistics; Machine learning; Psychology; Social psychology; Engineering","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":[0.003516025,0.001341263,0.001543143,0.002576984,0.001821583,0.002005461,0.00223777,0.001911193,0.003236217],"category_scores_gemma":[0.009038536,0.0005969597,0.001183894,0.002431832,0.001308596,0.003492158,0.002141582,0.002113413,0.0004099731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00227412,"about_ca_system_score_gemma":0.002799356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01401055,"about_ca_topic_score_gemma":0.008693297,"domain_scores_codex":[0.9978366,0.001005974,0.0000979147,0.0004680192,0.0004230494,0.0001684537],"domain_scores_gemma":[0.9965115,0.002436546,0.0002941321,0.0001140934,0.0005454355,0.0000983685],"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":[0.00008386193,0.00006972358,0.00135591,0.0001391117,0.00008119809,0.0001176224,0.0003369754,0.8443413,0.0009827956,0.04394145,0.00153128,0.1070188],"study_design_scores_gemma":[0.000007204787,0.00001846775,0.00007074654,0.000008271973,0.000006803852,0.00001368797,0.00002845766,0.9875998,0.0002091989,0.01136882,0.0006619465,0.00000654292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003930509,0.0001563065,0.9945905,0.0001270636,0.00001998437,0.00006070597,0.00001822772,0.0001222046,0.0009744545],"genre_scores_gemma":[0.2901054,0.0005091888,0.7043725,0.0002270723,0.00006652113,0.0008260073,0.0002327797,0.0001114778,0.003548995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01401055,"threshold_uncertainty_score":0.02785802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698120617941321,"score_gpt":0.3342009664028597,"score_spread":0.3172197602234464,"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."}}