{"id":"W2022518109","doi":"10.1038/ncomms6121","title":"A scaling law for random walks on networks","year":2014,"lang":"en","type":"article","venue":"Nature Communications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; McGill University; Ottawa Hospital","funders":"","keywords":"Random walk; Statistical physics; Complex network; Random graph; Continuous-time random walk; Nonlinear system; Power law; Exponential function; Computer science; Probability distribution; Scaling; Stochastic process; Path (computing); Mathematics; Physics; Theoretical computer science; Mathematical analysis; Combinatorics; Graph; Statistics","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":[0.001011798,0.0006846341,0.0007157373,0.002171695,0.000988038,0.002321458,0.0009565924,0.001557733,0.005351326],"category_scores_gemma":[0.01260944,0.0004682234,0.0006840504,0.001027994,0.002698521,0.004297304,0.0009808855,0.001948296,0.0009229422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052059,"about_ca_system_score_gemma":0.0005719764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00158111,"about_ca_topic_score_gemma":0.0008390925,"domain_scores_codex":[0.9993204,0.0001622991,0.00003039407,0.0001523322,0.0002458809,0.00008871184],"domain_scores_gemma":[0.9953902,0.002804875,0.0004594098,0.0003926424,0.0005929532,0.0003599105],"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":[0.00001059394,0.00001860086,0.0004861672,0.00006568668,0.00001239004,0.0001635352,0.0001519697,0.01604767,0.001658671,0.9718072,0.002654503,0.006922837],"study_design_scores_gemma":[0.00001113495,0.00001864918,0.0005387992,0.0000468234,0.000006310047,0.0002148405,0.00004085794,0.1685019,0.0002651396,0.8249393,0.005395226,0.00002097034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.11144,0.005947656,0.8151267,0.003967428,0.0007098985,0.000162339,0.0004775597,0.0009928044,0.06117559],"genre_scores_gemma":[0.9222221,0.005746996,0.05501777,0.000955342,0.001430851,0.0004616658,0.0004045829,0.0003696769,0.01339102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005351326,"threshold_uncertainty_score":0.01790202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429998231131488,"score_gpt":0.3106405238194915,"score_spread":0.2963405415081766,"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."}}