{"id":"W1992774762","doi":"10.1109/ciss.2014.6814136","title":"Microscopic generative models for complex networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Complex network; Generative grammar; Computer science; Human dynamics; Complex system; Evolving networks; Generative model; The Internet; Enhanced Data Rates for GSM Evolution; Data science; Theoretical computer science; Phenomenon; Small-world network; Artificial intelligence; World Wide Web; Epistemology","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":[0.001342448,0.0009545083,0.001003508,0.001755544,0.0008977507,0.002065099,0.001750207,0.001952623,0.01051267],"category_scores_gemma":[0.007410965,0.000563914,0.00121298,0.001540024,0.002399244,0.002679168,0.001653001,0.002201534,0.001432952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0020857,"about_ca_system_score_gemma":0.0008079511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00809664,"about_ca_topic_score_gemma":0.007139919,"domain_scores_codex":[0.9993844,0.0002560083,0.00002179797,0.0001215764,0.0001472575,0.00006889759],"domain_scores_gemma":[0.9970803,0.001965844,0.0003080792,0.0002236453,0.0002119314,0.0002101667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006769413,0.00001063073,0.0003131804,0.00003665357,0.00002074613,0.0000749562,0.0001321162,0.137922,0.0002838536,0.8550837,0.002449053,0.003666454],"study_design_scores_gemma":[0.000008855345,0.000005719219,0.0001491122,0.00001257715,0.00001006908,0.00006698107,0.00002729314,0.4181346,0.00004118057,0.5771317,0.004400345,0.00001143878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01929952,0.002121736,0.9435768,0.003083354,0.0001891211,0.0000711534,0.000748461,0.0005183933,0.03039141],"genre_scores_gemma":[0.8288742,0.006674191,0.1120267,0.001539424,0.0008766464,0.0006329337,0.001695611,0.000528485,0.04715178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01051267,"threshold_uncertainty_score":0.03516841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02714076824587968,"score_gpt":0.2832568401434916,"score_spread":0.2561160718976119,"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."}}