{"id":"W2258784158","doi":"10.1038/srep21297","title":"Assortativity and leadership emerge from anti-preferential attachment in heterogeneous networks","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Threat Reduction Agency; Universidad Rey Juan Carlos; Office of Naval Research; Azrieli Foundation; Israel Science Foundation; Ministerio de Economía y Competitividad","keywords":"Assortativity; Heterogeneous network; Preferential attachment; Degree (music); Population; Cluster analysis; Connection (principal bundle); Visibility","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005738634,0.0001699477,0.0002731261,0.0001127913,0.0001345306,0.0001557136,0.0001141826,0.0000425669,0.000794016],"category_scores_gemma":[0.0000076025,0.000128117,0.0001076104,0.0002484777,0.0001521249,0.0001445153,0.0001242978,0.00009580293,0.000006663327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003122759,"about_ca_system_score_gemma":0.00002549393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003229697,"about_ca_topic_score_gemma":0.0002899706,"domain_scores_codex":[0.9980474,0.00009244892,0.0004539919,0.0007687315,0.0002632383,0.0003742332],"domain_scores_gemma":[0.9989329,0.00005716528,0.0002483138,0.0006155117,0.00004522318,0.0001008715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000050646,0.0001144716,0.9647544,0.000002454244,0.00006915395,0.00005397953,0.0000916293,0.0001757461,0.00664999,0.0001452105,0.003979008,0.02395892],"study_design_scores_gemma":[0.001561882,0.0001080097,0.668551,0.0006064289,0.0003335046,0.00002573509,0.0002732057,0.01435377,0.1004129,0.177199,0.03431014,0.002264479],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9765902,0.0001939963,0.02159518,0.00008266424,0.0009416227,0.0001720363,0.000007018372,0.00005478247,0.0003624663],"genre_scores_gemma":[0.9988294,0.000003092222,0.0002253102,0.000007152275,0.0001691802,0.00002418025,0.00006600433,0.00001317197,0.0006625218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2962034,"threshold_uncertainty_score":0.8693919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04467035021560236,"score_gpt":0.2755235365625869,"score_spread":0.2308531863469846,"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."}}