Critical node identification in network cascading failure based on load percolation

级联故障 相互依存的网络 计算机科学 节点(物理) 渗透(认知心理学) 复杂网络 渗流理论 脆弱性(计算) 鉴定(生物学) 网络拓扑 关键基础设施 分布式计算 计算机网络 网络管理 拓扑(电路) 工程类 电力系统 计算机安全 功率(物理) 物理 植物 电气工程 结构工程 量子力学 神经科学 生物 万维网
作者
Hangyu Hu,Fan Wu,Xie Xiaowei,Qiang Wei,Xuemeng Zhai,Guangmin Hu
出处
期刊:Electronic research archive [American Institute of Mathematical Sciences]
卷期号:31 (3): 1524-1542 被引量:4
标识
DOI:10.3934/era.2023077
摘要

<abstract> <p>Identification of network vulnerability is one of the important means of cyberspace operation, management and security. As a typical case of network vulnerability, network cascading failures are often found in infrastructure networks such as the power grid system, communication network and road traffic, where the failure of a few nodes may cause devastating disasters to the whole complex system. Therefore, it is very important to identify the critical nodes in the network cascading failure and understand the internal laws of cascading failure in complex systems so as to fully grasp the vulnerability of complex systems and develop a network management strategy. The existing models for cascading failure analysis mainly evaluate the criticality of nodes by quantifying their importance in the network structure. However, they ignore the important load, node capacity and other attributes in the cascading failure model. In order to address those limitations, this paper proposes a novel critical node identification method in the load network from the perspective of a network adversarial attack. On the basis of obtaining a relatively complete topology, first, the network attack can be modeled as a cascading failure problem for the load network. Then, the concept of load percolation is proposed according to the percolation theory, which is used to construct the load percolation model in the cascading failure problem. After that, the identification method of critical nodes is developed based on the load percolation, which accurately identifies the vulnerable nodes. The experimental results show that the load percolation parameter can discover the affected nodes more accurately, and the final effect is better than those of the existing methods.</p> </abstract>

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