Influencer identification of dynamical networks based on an information entropy dimension reduction method  

作　　者：段东立 纪思源 袁紫薇 Dong-Li Duan;Si-Yuan Ji;Zi-Wei Yuan(School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an 710311,China;School of Mechanical Engineering,Northwestern Polytechnical University,Xi'an 710072,China)

机构地区：[1]School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an 710311,China [2]School of Mechanical Engineering,Northwestern Polytechnical University,Xi'an 710072,China

出　　处：《Chinese Physics B》2024年第4期375-384,共10页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Grant Nos.72071153 and 72231008);Laboratory of Science and Technology on Integrated Logistics Support Foundation (Grant No.6142003190102);the Natural Science Foundation of Shannxi Province (Grant No.2020JM486)。

摘　　要：Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

关 键 词：dynamical networks network influencer low-dimensional dynamics network disintegration 

分 类 号：O157.5[理学—数学]