Network theory has been widely used to describe complex systems in the real world. Identifying the influential nodes in a network is one of the most important topic in the research of network theory. Identification of influential nodes in a network is a significant and challenging task since influential nodes act as a hub for information transmission in a command and control network, however it is necessary if the invulnerability of the network is to be increased. The global and local structure (GLS) is a reasonable and efficacious method to quantify the influence of nodes in a network model. The influence of a node depends not only on its own influence but also on the ability of other nodes in the network to contribute to its influence. This method was also proved to be accurate and efficient in influential node analysis within a network. In this paper, influential node analysis is conducted in wheels and related networks based on GLS.

中文翻译：

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基于全局和局部结构的轮式网络影响节点识别

网络理论已被广泛用于描述现实世界中的复杂系统。识别网络中具有影响力的节点是网络理论研究中最重要的课题之一。网络中有影响力的节点的识别是一项重要且具有挑战性的任务，因为有影响力的节点充当命令和控制网络中信息传输的枢纽，但如果要提高网络的抗毁性，则有必要进行识别。全局和局部结构（GLS）是量化网络模型中节点影响力的合理且有效的方法。节点的影响力不仅取决于自身的影响力，还取决于网络中其他节点贡献其影响力的能力。该方法在网络内有影响力的节点分析中也被证明是准确和高效的。本文基于GLS对轮子及相关网络中的影响节点进行分析。