Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
Correctly measuring the importance of nodes in a complex network is critical for studying the robustness of the network, and designing a network security policy based on these highly important nodes can effectively improve security aspects of the network, such as the security of important data nodes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/4/1944 |
| _version_ | 1827657312276316160 |
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| author | Yongheng Zhang Yuliang Lu Guozheng Yang Zijun Hang |
| author_facet | Yongheng Zhang Yuliang Lu Guozheng Yang Zijun Hang |
| author_sort | Yongheng Zhang |
| collection | DOAJ |
| description | Correctly measuring the importance of nodes in a complex network is critical for studying the robustness of the network, and designing a network security policy based on these highly important nodes can effectively improve security aspects of the network, such as the security of important data nodes on the Internet or the hardening of critical traffic hubs. Currently included are degree centrality, closeness centrality, clustering coefficient, and H-index. Although these indicators can identify important nodes to some extent, they are influenced by a single evaluation perspective and have limitations, so most of the existing evaluation methods cannot fully reflect the node importance information. In this paper, we propose a multi-attribute critic network decision indicator (MCNDI) based on the CRITIC method, considering the H-index, closeness centrality, k-shell indicator, and network constraint coefficient. This method integrates the information of network attributes from multiple perspectives and provides a more comprehensive measure of node importance. An experimental analysis of the Chesapeake Bay network and the contiguous USA network shows that MCNDI has better ranking monotonicity, more stable metric results, and is highly adaptable to network topology. Additionally, deliberate attack simulations on real networks showed that the method exhibits high convergence speed in attacks on USAir97 networks and technology routes networks. |
| first_indexed | 2024-03-09T22:41:27Z |
| format | Article |
| id | doaj.art-3d569b0088bf46f89bc1341c5e87d150 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:41:27Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3d569b0088bf46f89bc1341c5e87d1502023-11-23T18:36:39ZengMDPI AGApplied Sciences2076-34172022-02-01124194410.3390/app12041944Multi-Attribute Decision Making Method for Node Importance Metric in Complex NetworkYongheng Zhang0Yuliang Lu1Guozheng Yang2Zijun Hang3Electronic Engineering Institute, National University of Defense Technology, Hefei 230037, ChinaElectronic Engineering Institute, National University of Defense Technology, Hefei 230037, ChinaElectronic Engineering Institute, National University of Defense Technology, Hefei 230037, ChinaElectronic Engineering Institute, National University of Defense Technology, Hefei 230037, ChinaCorrectly measuring the importance of nodes in a complex network is critical for studying the robustness of the network, and designing a network security policy based on these highly important nodes can effectively improve security aspects of the network, such as the security of important data nodes on the Internet or the hardening of critical traffic hubs. Currently included are degree centrality, closeness centrality, clustering coefficient, and H-index. Although these indicators can identify important nodes to some extent, they are influenced by a single evaluation perspective and have limitations, so most of the existing evaluation methods cannot fully reflect the node importance information. In this paper, we propose a multi-attribute critic network decision indicator (MCNDI) based on the CRITIC method, considering the H-index, closeness centrality, k-shell indicator, and network constraint coefficient. This method integrates the information of network attributes from multiple perspectives and provides a more comprehensive measure of node importance. An experimental analysis of the Chesapeake Bay network and the contiguous USA network shows that MCNDI has better ranking monotonicity, more stable metric results, and is highly adaptable to network topology. Additionally, deliberate attack simulations on real networks showed that the method exhibits high convergence speed in attacks on USAir97 networks and technology routes networks.https://www.mdpi.com/2076-3417/12/4/1944complex networksnode importance metricmulti-attribute integrated measurementCRITIC method |
| spellingShingle | Yongheng Zhang Yuliang Lu Guozheng Yang Zijun Hang Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network Applied Sciences complex networks node importance metric multi-attribute integrated measurement CRITIC method |
| title | Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network |
| title_full | Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network |
| title_fullStr | Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network |
| title_full_unstemmed | Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network |
| title_short | Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network |
| title_sort | multi attribute decision making method for node importance metric in complex network |
| topic | complex networks node importance metric multi-attribute integrated measurement CRITIC method |
| url | https://www.mdpi.com/2076-3417/12/4/1944 |
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