Self-similar transformations of complex networks and their applications
发布日期:2025-08-04
作者:
编辑:lqx
来源:
报告人:郑木华 教授(江苏大学)
题目:Self-similar transformations of complex networks and their applications
时间:2025年8月6日(周三)上午10:30
地点:理工楼1215
邀请人:王宜森
报告摘要:
Symmetries in physical theories describe invariance under specific transformations, such as self-similarity across different scales. The renormalization group offers a powerful framework to study such symmetries. In this work, we propose a framework for analyzing complex networks across multiple resolutions. We first introduce a geometric renormalization (GR) protocol that systematically reduces the resolution of a network, generating scaled-down replicas that reveal its multiscale structure. This approach uncovers the self-similar properties of complex systems, including human connectomes. We then extend the GR framework to incorporate weighted networks, enabling a broader application. Finally, we introduce an inverse renormalization procedure within the Geometric Branching Growth (GBG) model, designed to capture the self-similar branching dynamics observed in the growth of real-world networks and to explain the underlying symmetries in their evolution.
个人简介:
郑木华,教授,博士生导师,2022年获评江苏特聘教授。2017年博士毕业于华东师范大学,2015至2016年在美国纽约城市大学Hernán Makse教授课题组联合培养,2017至2020年在巴塞罗那大学Marián Boguñá和M. Ángeles Serrano教授课题组从事博士后研究工作。研究方向为非线性物理与复杂网络、脑科学、复杂网络在双曲空间中的映射及应用、网络传播动力学等。目前已在PNAS, Nat. Commun., Commun. Phys., Phys. Rev. E等国际著名期刊上发表论文40余篇。相关研究成果被AAAS EurekAlert,News Medical,MedicalXpress,News wise,Technologynetworks等多家在学术界有重要影响力的杂志和媒体专门报道。先后主持/参与欧盟、西班牙及中国国家自然科学基金6项。目前主持江苏特聘教授人才项目1项。