{"id":"W2112804575","doi":"10.1145/2492517.2492633","title":"Incremental local community identification in dynamic social networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Soundness; Computer science; Identification (biology); Community structure; Dynamic network analysis; Key (lock); Frame (networking); Population; Data mining; Data science; Artificial intelligence; Computer security; Mathematics; Computer network; Ecology; Sociology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002193408,0.00008367917,0.0001210055,0.00005499849,0.0001350796,0.00006216428,0.0001884929,0.00002748501,0.00155635],"category_scores_gemma":[5.946276e-7,0.00008461391,0.00006261266,0.0002056266,0.00005475682,0.0001405002,0.0001102339,0.0002633881,0.00006306392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000647148,"about_ca_system_score_gemma":0.000006837244,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009922354,"about_ca_topic_score_gemma":0.0009209299,"domain_scores_codex":[0.9992842,0.0001499427,0.0002315954,0.00009545888,0.00007739887,0.0001613919],"domain_scores_gemma":[0.9996689,0.00003210648,0.00006195479,0.0001806999,0.0000325347,0.000023807],"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":[0.00001150096,0.00101901,0.2844235,0.000008276435,0.0001611765,3.698413e-7,0.0009879398,0.003574076,0.0026956,0.04028932,0.02207778,0.6447515],"study_design_scores_gemma":[0.0002447016,0.000013071,0.4153388,0.000006775773,0.00001620165,1.145776e-7,0.001860669,0.5571524,0.000311395,0.02467937,0.0001702996,0.0002062317],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5906168,0.000004574691,0.4034568,0.00008153004,0.00001541641,0.0001461029,0.000001001069,0.00005109678,0.005626719],"genre_scores_gemma":[0.9992993,4.885366e-7,0.0002623711,0.00004013169,0.00005938943,0.00006512317,0.0001054067,0.000008208658,0.000159583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6445453,"threshold_uncertainty_score":0.9993564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009529289300298026,"score_gpt":0.2673468518280712,"score_spread":0.2578175625277732,"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."}}