Research on the Robustness of Complex Networks Based on Dynamic Control of Node Redundancy Capacity

Zhen ZHANG; Diyang LIU; Jin ZHANG; Jichao XIE

doi:10.11999/JEIT200185

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1349-1356

Zhen ZHANG, Diyang LIU, Jin ZHANG, Jichao XIE. Research on the Robustness of Complex Networks Based on Dynamic Control of Node Redundancy Capacity[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1349-1356. doi: 10.11999/JEIT200185

Citation:

Zhen ZHANG, Diyang LIU, Jin ZHANG, Jichao XIE. Research on the Robustness of Complex Networks Based on Dynamic Control of Node Redundancy Capacity[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1349-1356. doi: 10.11999/JEIT200185

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Research on the Robustness of Complex Networks Based on Dynamic Control of Node Redundancy Capacity

doi: 10.11999/JEIT200185 cstr: 32379.14.JEIT200185

1.
PLA Strategic Support Force Information Engineering University, Zhengzhou 450000, China
2.
Network Communication and Security Purple Mountain Laboratory, Nanjing 210000, China

Funds: The National Natural Science Foundation of China(61802429, 61872382, 61521003), The National Key Research and Development Plan(2017YFB0803201, 2017YFB0803204)

Received Date: 2020-03-20
Rev Recd Date: 2020-09-02

Available Online: 2020-09-17

Publish Date: 2021-05-18

Abstract

Abstract

In View of the problem of fixed redundancy parameters in the traditional cascade failure model, this paper comprehensively considers the different attack levels of nodes and the dynamic changes of the network topology during the failure process, and establishes a cascading failure model based on Dynamic control of node Redundancy Capacity (DRC). By defining the critical factor $\theta $ of the phase transition of the network to measure the probability of node failure leading to cascading failure, the correlation between network robustness and $\theta $ is analyzed, and the analytic expression of $\theta $ is derived in detail by combining degree distribution function, Based on analytic expressions, two network robustness enhancement strategies are proposed. The simulation results show that in model network and real network, the robustness of target network can be effectively improved by adjusting the initial load parameter $\tau $ of nodes according to the difference of degree of nodes under attack. The failure propagation range of DRC model is significantly reduced compared with Motter-Lai (ML) model.
- Complex networks,
- Cascade failure,
- Robustness,
- Node redundancy capacity

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References(13)

References

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