{"id":"W2005989548","doi":"10.1088/1367-2630/17/1/013044","title":"Defining and identifying cograph communities in complex networks","year":2015,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Centrality; Enhanced Data Rates for GSM Evolution; Community structure; Complex network; Computer science; Cograph; Focus (optics); Representation (politics); Simple (philosophy); Theoretical computer science; Identification (biology); Graph; Algorithm; Artificial intelligence; Combinatorics; Mathematics; Physics; Political science; Epistemology; World Wide Web","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":[0.0003795315,0.0001176708,0.0003208216,0.0001036842,0.00005868842,0.00009298413,0.000201094,0.00002064489,0.00002962863],"category_scores_gemma":[0.000003144632,0.0001141393,0.0001094026,0.0002959818,0.00006002786,0.0002388959,0.00009908021,0.0003510343,0.000001061718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002108854,"about_ca_system_score_gemma":0.00005540113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006438103,"about_ca_topic_score_gemma":0.00007132033,"domain_scores_codex":[0.9991211,0.00009775354,0.0003698885,0.00006088011,0.0001743111,0.0001760261],"domain_scores_gemma":[0.9991708,0.0001071764,0.0003262108,0.0001409205,0.0001258246,0.0001290431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005051133,0.0001575107,0.6279217,0.000008811625,0.0002495906,0.000007767259,0.003307953,0.01662677,0.00006788972,0.08146968,0.01816549,0.2519663],"study_design_scores_gemma":[0.006012993,0.000523652,0.05495841,0.0008496037,0.0004810181,0.00004595199,0.01898978,0.1087222,0.0005204676,0.7889141,0.01886592,0.001115869],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4641081,0.002011929,0.5290249,0.0001143332,0.0001327652,0.00009688806,0.00000334152,0.00002773066,0.004479998],"genre_scores_gemma":[0.9925284,0.00002977043,0.006884699,0.00004153839,0.0004768321,7.455683e-7,0.00001196031,0.00001331217,0.00001269052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7074444,"threshold_uncertainty_score":0.4654464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06252222761273765,"score_gpt":0.3107602615749357,"score_spread":0.2482380339621981,"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."}}