{"id":"W2030794512","doi":"10.1145/1569901.1570068","title":"Agglomerative genetic algorithm for clustering in social networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University; Arizona Cotton Growers Association","keywords":"Cluster analysis; Computer science; Social network (sociolinguistics); Hierarchical clustering of networks; Data mining; CURE data clustering algorithm; Correlation clustering; Canopy clustering algorithm; Hierarchical clustering; Single-linkage clustering; Hierarchical network model; Brown clustering; Machine learning; Artificial intelligence; Social media; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006914462,0.0001021438,0.0001754544,0.00005420687,0.00009166597,0.00003690317,0.00009231652,0.00002588838,0.0001384613],"category_scores_gemma":[4.133975e-7,0.0001005643,0.0001040241,0.0001781505,0.00001171147,0.00004335075,0.0000242905,0.0000763865,0.000001621071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002207194,"about_ca_system_score_gemma":0.000008680164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004784822,"about_ca_topic_score_gemma":0.00001943923,"domain_scores_codex":[0.9993549,0.00002024553,0.000181845,0.0001672881,0.00005083662,0.000224913],"domain_scores_gemma":[0.9997937,0.00002648299,0.00004501991,0.00008142817,0.00002896524,0.00002437903],"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":[0.000003561223,0.0000524213,0.0009932817,4.61314e-7,0.00002189371,4.139246e-7,0.00008187344,0.004632875,0.00001772379,0.003767017,0.002909946,0.9875185],"study_design_scores_gemma":[0.0002941245,0.00004679784,0.007295711,0.000005702839,0.00001388393,1.205772e-7,0.00005949066,0.9725041,0.00009549196,0.01823006,0.001298612,0.0001558708],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002423154,0.0000269271,0.9901633,0.0001065872,0.00001886204,0.0002146359,0.000002414914,0.00004598342,0.006998098],"genre_scores_gemma":[0.9058262,0.00000133099,0.09291308,0.0001144147,0.0008033868,0.0000430092,0.00002001863,0.000007782962,0.0002708441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9873627,"threshold_uncertainty_score":0.4100894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248469075954827,"score_gpt":0.2879179675385456,"score_spread":0.2754332767789974,"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."}}