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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 |
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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.
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Received: 11 September 2023
Revised: 17 November 2023
Accepted manuscript online: 28 November 2023
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PACS:
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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89.75.Fb
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(Structures and organization in complex systems)
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87.55.kd
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(Algorithms)
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89.75.Hc
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(Networks and genealogical trees)
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| Fund: 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). |
Corresponding Authors:
Dong-Li Duan
E-mail: mineduan@163.cn
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Cite this article:
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 2024 Chin. Phys. B 33 040502
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