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

doi:10.1088/1674-1056/ad102e

中国物理B ›› 2024, Vol. 33 ›› Issue (4): 40502-040502.doi: 10.1088/1674-1056/ad102e

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

Dong-Li Duan(段东立)^1,†, Si-Yuan Ji(纪思源)¹, and Zi-Wei Yuan(袁紫薇)²

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

收稿日期:2023-09-11 修回日期:2023-11-17 接受日期:2023-11-28 出版日期:2024-03-19 发布日期:2024-03-19
通讯作者: Dong-Li Duan E-mail:mineduan@163.cn
基金资助:
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), and the Natural Science Foundation of Shannxi Province (Grant No. 2020JM- 486).

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

Dong-Li Duan(段东立)^1,†, Si-Yuan Ji(纪思源)¹, and Zi-Wei Yuan(袁紫薇)²

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

Received:2023-09-11 Revised:2023-11-17 Accepted:2023-11-28 Online:2024-03-19 Published:2024-03-19
Contact: Dong-Li Duan E-mail:mineduan@163.cn
Supported by:
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), and the Natural Science Foundation of Shannxi Province (Grant No. 2020JM- 486).

摘要/Abstract

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

Abstract: 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.

Key words: dynamical networks, network influencer, low-dimensional dynamics, network disintegration

中图分类号: (Computational methods in statistical physics and nonlinear dynamics)

05.10.-a

89.75.Fb (Structures and organization in complex systems) 87.55.kd (Algorithms) 89.75.Hc (Networks and genealogical trees)

Dong-Li Duan(段东立), Si-Yuan Ji(纪思源), and Zi-Wei Yuan(袁紫薇). Influencer identification of dynamical networks based on an information entropy dimension reduction method[J]. 中国物理B, 2024, 33(4): 40502-040502.

Dong-Li Duan(段东立), Si-Yuan Ji(纪思源), and Zi-Wei Yuan(袁紫薇). Influencer identification of dynamical networks based on an information entropy dimension reduction method[J]. Chin. Phys. B, 2024, 33(4): 40502-040502.

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

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

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