{"id":"W2276717191","doi":"10.1002/cpe.3773","title":"Parallel social network mining for interesting ‘following’ patterns","year":2016,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Interdependence; Computer science; Friendship; Social network (sociolinguistics); Social network analysis; Data science; Data mining; World Wide Web; Social media; Sociology","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":[0.0002293627,0.00009951494,0.000107045,0.00002321407,0.0004523948,0.0002283086,0.0001896657,0.00003294626,0.00000139916],"category_scores_gemma":[0.0002097194,0.00007933738,0.00002592055,0.0001106482,0.00005285853,0.00135935,0.0001438896,0.00004264728,0.000002068911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007487795,"about_ca_system_score_gemma":0.0000256289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007277884,"about_ca_topic_score_gemma":9.927331e-7,"domain_scores_codex":[0.999118,0.00003906728,0.0001952927,0.0003561612,0.00009536089,0.0001960859],"domain_scores_gemma":[0.998857,0.0007715048,0.0001429111,0.00009857662,0.0000673223,0.00006264458],"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":[0.00000659163,0.00002140022,0.0008367224,0.000009475156,0.00001132491,0.000002506947,0.008457304,0.000006302376,0.00006043346,0.02405465,0.0005750544,0.9659582],"study_design_scores_gemma":[0.007385179,0.001238127,0.02438842,0.001139737,0.0001765854,0.0003678159,0.02777453,0.7121853,0.0003048804,0.03432896,0.1879774,0.00273314],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07457697,0.000226747,0.9226915,0.001931602,0.0002597015,0.0001143775,0.000006037448,0.00006246186,0.0001306355],"genre_scores_gemma":[0.8651753,0.00005250329,0.1341514,0.0003608,0.0001260728,0.00009847501,0.000005309419,0.000004586556,0.00002556887],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9632251,"threshold_uncertainty_score":0.34795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0467482113840547,"score_gpt":0.3547713504565648,"score_spread":0.3080231390725101,"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."}}