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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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021088/ |
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| _version_ | 1841539254002384896 |
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| author | Qiuyang GU Bao WU Zhaoyang SUN Renyong CHI |
| author_facet | Qiuyang GU Bao WU Zhaoyang SUN Renyong CHI |
| author_sort | Qiuyang GU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0774b12c3755423391c4ec99dcadb51c |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-0774b12c3755423391c4ec99dcadb51c2025-01-14T07:22:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-06-0142728359741964Key node identification algorithm for complex network based on improved grey wolf optimizationQiuyang GUBao WUZhaoyang SUNRenyong CHIIn 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021088/improved grey wolf optimizationcomplex networkkey node identificationthe maximization of influence |
| spellingShingle | Qiuyang GU Bao WU Zhaoyang SUN Renyong CHI Key node identification algorithm for complex network based on improved grey wolf optimization Tongxin xuebao improved grey wolf optimization complex network key node identification the maximization of influence |
| title | Key node identification algorithm for complex network based on improved grey wolf optimization |
| title_full | Key node identification algorithm for complex network based on improved grey wolf optimization |
| title_fullStr | Key node identification algorithm for complex network based on improved grey wolf optimization |
| title_full_unstemmed | Key node identification algorithm for complex network based on improved grey wolf optimization |
| title_short | Key node identification algorithm for complex network based on improved grey wolf optimization |
| title_sort | key node identification algorithm for complex network based on improved grey wolf optimization |
| topic | improved grey wolf optimization complex network key node identification the maximization of influence |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021088/ |
| work_keys_str_mv | AT qiuyanggu keynodeidentificationalgorithmforcomplexnetworkbasedonimprovedgreywolfoptimization AT baowu keynodeidentificationalgorithmforcomplexnetworkbasedonimprovedgreywolfoptimization AT zhaoyangsun keynodeidentificationalgorithmforcomplexnetworkbasedonimprovedgreywolfoptimization AT renyongchi keynodeidentificationalgorithmforcomplexnetworkbasedonimprovedgreywolfoptimization |