{"id":"W2768874478","doi":"10.1109/tsmc.2017.2693026","title":"Expected Convergence Rate to Consensus in Asymmetric Networks: Analysis and Distributed Estimation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"Public Works and Government Services Canada","keywords":"Laplacian matrix; Rate of convergence; Convergence (economics); Algebraic connectivity; Eigenvalues and eigenvectors; Mathematics; Power iteration; Subspace topology; Graph; Computer science; Krylov subspace; Mathematical optimization; Algorithm; Iterative method; Discrete mathematics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009727349,0.0004138468,0.0008607691,0.000907425,0.0005410297,0.001703498,0.0007600966,0.0002214188,0.000001638288],"category_scores_gemma":[0.00006104365,0.0004120389,0.0001165893,0.001432162,0.0001088922,0.0002935276,0.00001897145,0.0002487509,0.00003066427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826365,"about_ca_system_score_gemma":0.00004448398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004187134,"about_ca_topic_score_gemma":0.000513502,"domain_scores_codex":[0.996565,0.0005240152,0.0009749491,0.0009298274,0.0004507129,0.0005555354],"domain_scores_gemma":[0.9970527,0.000351757,0.0005449174,0.001413439,0.0002267876,0.0004103986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008221959,0.000219861,0.00817003,0.0002386728,0.0008866097,0.0001201012,0.0007350892,0.9789645,0.0003583376,0.003537689,0.0002873968,0.006399473],"study_design_scores_gemma":[0.001156768,0.0001122435,0.02796718,0.0002318495,0.0001729285,0.0000449757,0.0002835039,0.969292,0.00009149336,0.000009307643,0.0001975797,0.0004401658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07373135,0.000521889,0.9218232,0.0001704335,0.002074911,0.001212726,0.0001513041,0.0001637255,0.0001504578],"genre_scores_gemma":[0.9988639,0.00006265489,0.0002911596,0.00002264813,0.00005118727,0.0002644313,0.00001362361,0.00002280575,0.0004075644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9251326,"threshold_uncertainty_score":0.9998332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535096521814986,"score_gpt":0.2465913507384045,"score_spread":0.2312403855202547,"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."}}