Improving controllability of symmetric network based on time segmentation

被引：0
作者：
Wang, Li-Fu [1 ]
Kou, Xiao-Yu [1 ]
Kong, Zhi [1 ]
Guo, Ge [1 ]
机构：
[1] School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao
来源：
Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 08期
关键词：
controllability; maximum matching; symmetric network; time-varying network; undirected cactus;
D O I：
10.13195/j.kzyjc.2023.0202
中图分类号：
学科分类号：
摘要：
Network structure has a great impact on the realization of complete network control. Therefore, it is of great significance to optimize the controllability of complex networks only based on network structure without adding drive nodes. This paper proposes a method of dividing a static symmetric network into a dynamic time-varying network composed of multiple snapshots (each snapshot is a static network), which uses the advantages of the time-varying network to reduce the number of driving nodes and improve the network controllability. The controllability criteria of the time-varying symmetric network composed of multiple snapshots, the optimal partition of snapshots, and the relationship between the number of drive nodes and the number of snapshots are given. The application process of the partition method is illustrated by an actual example, and the simulation results are verified in the model network and the real network. The results show that the method of time segmentation can effectively reduce the number of driving nodes in the symmetric network and improve the network controllability. © 2024 Northeast University. All rights reserved.
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页码：2671 / 2678
页数：7
相关论文
共 23 条
  • [1] Chen D X, Shao Q, Liu Z Y, Et al., Ridesourcing behavior analysis and prediction: A network perspective, IEEE Transactions on Intelligent Transportation Systems, 23, 2, pp. 1274-1283, (2022)
  • [2] Wang L F, Zhu F, Guo G, Et al., Control Station identification of rail transport network based on loading coefficient, Control and Decision, 35, 10, pp. 2319-2328, (2020)
  • [3] Wang L F, Zhong H N, Guo G., Identification of key road sections of road traffic network based on congestion coefficient, Control and Decision, 38, 3, pp. 843-849, (2023)
  • [4] Chen G R, Lou Y., Naming game: Models, simulations and analysis, (2019)
  • [5] Firth J A, Hellewell J, Klepac P, Et al., Using a real-world network to model localized COVID-19 control strategies, Nature Medicine, 26, 10, pp. 1616-1622, (2020)
  • [6] Zhang Z K, Liu C, Zhan X X, Et al., Dynamics of information diffusion and its applications on complex networks, Physics Reports, 651, pp. 1-34, (2016)
  • [7] Liu Y Y, Slotine J J, Barabasi A L., Controllability of complex networks, Nature, 473, 7346, pp. 167-173, (2011)
  • [8] Yuan Z Z, Zhao C, Di Z R, Et al., Exact controllability of complex networks, Nature Communications, 4, 1, pp. 2447-2455, (2013)
  • [9] Nepusz T, Vicsek T., Controlling edge dynamics in complex networks, Nature Physics, 8, 7, pp. 568-573, (2012)
  • [10] Wang W X, Ni X, Lai Y C, Et al., Optimizing controllability of complex networks by minimum structural perturbations, Physical Review E: Statistical Nonlinear & Soft Matter Physics, 85, 2, (2012)
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