{"id":"W1488446430","doi":"10.1002/rsa.20539","title":"The mixing time of the giant component of a random graph","year":2014,"lang":"en","type":"article","venue":"Random Structures and Algorithms","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Expander graph; Mixing (physics); Random graph; Random walk; Mathematics; Combinatorics; Constant (computer programming); Vertex (graph theory); Component (thermodynamics); Exponential function; Discrete mathematics; Graph; Statistical physics; Computer science; Physics; Statistics; Mathematical analysis; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000998082,0.0004232079,0.0005395469,0.001606266,0.0007332696,0.001169816,0.0009896929,0.0008685212,0.003086694],"category_scores_gemma":[0.008280764,0.0004181658,0.0005070296,0.0005335268,0.002036645,0.001730204,0.001376645,0.0008080986,0.00022957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001262517,"about_ca_system_score_gemma":0.0004066142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00217259,"about_ca_topic_score_gemma":0.001560024,"domain_scores_codex":[0.999608,0.0001227216,0.00001320378,0.0001017256,0.0000907946,0.00006361611],"domain_scores_gemma":[0.9967437,0.001829407,0.0004056164,0.0002122788,0.0002343025,0.0005745871],"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":[0.0003083393,0.00004642312,0.006874146,0.0001632605,0.0001418307,0.0004997664,0.0004951488,0.1292338,0.01991295,0.828392,0.002793229,0.01113913],"study_design_scores_gemma":[0.00002863838,0.00004922513,0.002741455,0.00003750157,0.00004121666,0.0002116427,0.00007712388,0.731237,0.004071643,0.2598553,0.001602047,0.00004721913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8143937,0.0005164596,0.1721253,0.0009116731,0.00008373645,0.00006391773,0.0002097845,0.0003994615,0.01129591],"genre_scores_gemma":[0.9902061,0.0001129118,0.007201837,0.00007256726,0.00003285196,0.00004437098,0.0001184947,0.00007097478,0.002139937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003086694,"threshold_uncertainty_score":0.01032597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01511420349440316,"score_gpt":0.2716785362266982,"score_spread":0.256564332732295,"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."}}