Identifying key nodes in complex networks based on an improved gravity model
The identification of key nodes in complex networks is a hot topic. Therefore, it attracts increasing attention from different fields, like airline networks and social networks. To identify the key nodes in complex network, we suggest an improved gravity model method that takes propagation features...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2023.1239660/full |
| _version_ | 1797755554267922432 |
|---|---|
| author | Linfeng Zhong Linfeng Zhong Xiangying Gao Liang Zhao Lei Zhang Pengfei Chen Hao Yang Jin Huang Weijun Pan |
| author_facet | Linfeng Zhong Linfeng Zhong Xiangying Gao Liang Zhao Lei Zhang Pengfei Chen Hao Yang Jin Huang Weijun Pan |
| author_sort | Linfeng Zhong |
| collection | DOAJ |
| description | The identification of key nodes in complex networks is a hot topic. Therefore, it attracts increasing attention from different fields, like airline networks and social networks. To identify the key nodes in complex network, we suggest an improved gravity model method that takes propagation features into account. Relevant experiments were carried out in four actual airline networks based on the Susceptible Infected Recovered (SIR) model. First, we analyze the correlation between the proposed method and other benchmark methods.Then, Kendall’s correlation coefficient and the imprecision function were used as evaluation metrics to analyze and validate the proposed method. Empirical results reveal that the suggested method outperforms previous benchmark methods in terms of precision and effectiveness for identifying key nodes, especially in the US air network, where Kendall’s tau achieves a 107% improvement compared to the gravity centrality method. |
| first_indexed | 2024-03-12T17:47:59Z |
| format | Article |
| id | doaj.art-d5bd075ff83849799fb74e91bcc8435d |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-03-12T17:47:59Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-d5bd075ff83849799fb74e91bcc8435d2023-08-03T12:06:25ZengFrontiers Media S.A.Frontiers in Physics2296-424X2023-08-011110.3389/fphy.2023.12396601239660Identifying key nodes in complex networks based on an improved gravity modelLinfeng Zhong0Linfeng Zhong1Xiangying Gao2Liang Zhao3Lei Zhang4Pengfei Chen5Hao Yang6Jin Huang7Weijun Pan8School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaChengdu GoldTel Industry Group Co., Ltd., Chengdu, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaOperation Management Center of ATMB, Civil Aviation Administration of China, Beijing, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaSchool of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, ChinaThe identification of key nodes in complex networks is a hot topic. Therefore, it attracts increasing attention from different fields, like airline networks and social networks. To identify the key nodes in complex network, we suggest an improved gravity model method that takes propagation features into account. Relevant experiments were carried out in four actual airline networks based on the Susceptible Infected Recovered (SIR) model. First, we analyze the correlation between the proposed method and other benchmark methods.Then, Kendall’s correlation coefficient and the imprecision function were used as evaluation metrics to analyze and validate the proposed method. Empirical results reveal that the suggested method outperforms previous benchmark methods in terms of precision and effectiveness for identifying key nodes, especially in the US air network, where Kendall’s tau achieves a 107% improvement compared to the gravity centrality method.https://www.frontiersin.org/articles/10.3389/fphy.2023.1239660/fullcomplex networkkey nodesgravity modelpropagation featureSIR |
| spellingShingle | Linfeng Zhong Linfeng Zhong Xiangying Gao Liang Zhao Lei Zhang Pengfei Chen Hao Yang Jin Huang Weijun Pan Identifying key nodes in complex networks based on an improved gravity model Frontiers in Physics complex network key nodes gravity model propagation feature SIR |
| title | Identifying key nodes in complex networks based on an improved gravity model |
| title_full | Identifying key nodes in complex networks based on an improved gravity model |
| title_fullStr | Identifying key nodes in complex networks based on an improved gravity model |
| title_full_unstemmed | Identifying key nodes in complex networks based on an improved gravity model |
| title_short | Identifying key nodes in complex networks based on an improved gravity model |
| title_sort | identifying key nodes in complex networks based on an improved gravity model |
| topic | complex network key nodes gravity model propagation feature SIR |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2023.1239660/full |
| work_keys_str_mv | AT linfengzhong identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT linfengzhong identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT xiangyinggao identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT liangzhao identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT leizhang identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT pengfeichen identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT haoyang identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT jinhuang identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel AT weijunpan identifyingkeynodesincomplexnetworksbasedonanimprovedgravitymodel |