Key node identification algorithm for complex network based on improved grey wolf optimization

In recent years, how to select the most influential key node for identification has become the most cutting-edge hot direction in network science.Formulating the problem of maximizing the influence of complex network nodes as an optimization problem whose cost function was expressed as the influence...

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Hlavní autoři: Qiuyang GU, Bao WU, Zhaoyang SUN, Renyong CHI
Médium: Článek
Jazyk:zho
Vydáno: Editorial Department of Journal on Communications 2021-06-01
Edice:Tongxin xuebao
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On-line přístup:http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021088/
Popis
Shrnutí:In recent years, how to select the most influential key node for identification has become the most cutting-edge hot direction in network science.Formulating the problem of maximizing the influence of complex network nodes as an optimization problem whose cost function was expressed as the influence of nodes and the distance between them, measures user influence using Shannon entropy, and solved this problem using an improved gray wolf optimization algorithm.Finally, numerical examples were performed with real complex network datasets.The experimental results show that the proposed algorithm is more accurate and computationally efficient than the existing method.
ISSN:1000-436X