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...
| Autors principals: | , , , |
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| Format: | Article |
| Idioma: | zho |
| Publicat: |
Editorial Department of Journal on Communications
2021-06-01
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| Col·lecció: | Tongxin xuebao |
| Matèries: | |
| Accés en línia: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021088/ |
| Sumari: | 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. |
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| ISSN: | 1000-436X |