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Self-similar transformation of complex network and its application
报告人:郑木华教授,江苏大学 时间:2022年1月14日19:00 字号:

腾讯会议:602-663-5703


报告摘要:Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries. Here, we provide a framework for the investigation of complex networks at different resolutions. Firstly, we introduce a geometric renormalization (GR) protocol by decreasing the resolution in complex network. Then we show that the GR model produces a multiscale unfolding of the network in scaled-down replicas, which can be used to predict the multiscale self-similar properties of human connectomes. In the end, we present an inverse renormalization transformation in the Geometric Branching Growth (GBG) model, which is designed to predict the self-similar branching growth in the evolution of real networks and explain the symmetries observed. Practical applications of GBG model in real instances include the tuning of network size for best response to external influence and finite-size scaling to assess critical behaviour under random link failures.

报告人简介: 郑木华,江苏大学物理与电子工程学院教授,硕士生导师。2017年博士毕业于华东师范大学,20152016年在美国纽约城市大学Hernán Makse教授课题组联合培养,20172020年在巴塞罗那大学Marián BoguñáM. 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、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PNASPhys. Rev. E Natl. Sci. Rev.等国际著名期刊上发表论文近30篇,其中第1/通讯作者11篇。相关研究成果被AAAS EurekAlertNews MedicalMedicalXpressNews wiseTechnologynetworks 等多家在学术界有重要影响力的杂志和媒体专门报道。先后参与欧盟、西班牙及中国国家自然科学基金5项。目前主持国家青年基金和江苏大学高层次人才启动基金各1项。


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