Rigorous mean-field dimensional reduction in heterogeneous network dynamics

This thesis presents phenomenological and theoretical studies of a class of heterogeneous random networks, where the network degree distribution follows a power-law, and each node dynamics is a random dynamical system, interacting with neighboring nodes via a random coupling function. We characteriz...

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Detalles Bibliográficos
Autor: Bian, Zheng
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2024
País:Brasil
Institución:Universidade de São Paulo (USP)
Repositorio:Biblioteca Digital de Teses e Dissertações da USP
Idioma:inglés
OAI Identifier:oai:teses.usp.br:tde-30092024-145313
Acceso en línea:https://www.teses.usp.br/teses/disponiveis/55/55134/tde-30092024-145313/
Access Level:acceso abierto
Palabra clave:Dinâmicas em redes
Grafos aleatórios
Heterogeneous networks
Network dynamics
Random dynamical systems
Random graphs
Redes heterogêneas
Redução de campo médio rigorosa
Rigorous mean-field reduction
Sistemas dinâmicos aleatórios
Descripción
Sumario:This thesis presents phenomenological and theoretical studies of a class of heterogeneous random networks, where the network degree distribution follows a power-law, and each node dynamics is a random dynamical system, interacting with neighboring nodes via a random coupling function. We characterize the hub behavior by the mean-field, subject to statistically controlled fluctuations. In particular, we prove that the fluctuations are small over exponentially long time scales and obtain Berry-Esseen estimates for the fluctuation statistics at any fixed time. Our results provide an explanation for several numerical observations, namely, a scaling relation between system size and frequency of large fluctuations, the system size induced desynchronization, and the Gaussian behavior of the fluctuations. Some fundamental results from random graphs, network dynamics, Markov chains, and random dynamical systems are reviewed and reinterpreted.