Identification of Influential Nodes in Industrial Networks Based on Structure Analysis
Industrial network systems are facing various new challenges, such as increasing functional failure factors, the accelerating penetration of information threats, and complex and diverse attack methods. Industrial networks are often vulnerable to natural or intentional disasters; therefore, it is hig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/2/211 |
| _version_ | 1797476478065049600 |
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| author | Tianyu Wang Peng Zeng Jianming Zhao Xianda Liu Bowen Zhang |
| author_facet | Tianyu Wang Peng Zeng Jianming Zhao Xianda Liu Bowen Zhang |
| author_sort | Tianyu Wang |
| collection | DOAJ |
| description | Industrial network systems are facing various new challenges, such as increasing functional failure factors, the accelerating penetration of information threats, and complex and diverse attack methods. Industrial networks are often vulnerable to natural or intentional disasters; therefore, it is highly invaluable to research to identify the influential nodes. Most of the state-of-the-art evaluates the importance of the nodes according to one or more network metrics. Moreover, there are no metrics reflecting all the properties of the network. In this paper, a novel method (Structure-based Identification Method, SIM) to identify the influential nodes in industrial networks is proposed based on the network structure, which goes beyond the use of network metrics. The SIM method extracts the weakly connected components, which are more likely to survive after the important nodes are attacked in the network. Evaluation results show that the SIM method obtains better results than the state-of-the-art methods to identify influential nodes in real-world industrial networks and has a good prospect to be applied in industrial application. |
| first_indexed | 2024-03-09T20:58:23Z |
| format | Article |
| id | doaj.art-706f79f9d48041bba47f77e70520dfb0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T20:58:23Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-706f79f9d48041bba47f77e70520dfb02023-11-23T22:15:05ZengMDPI AGSymmetry2073-89942022-01-0114221110.3390/sym14020211Identification of Influential Nodes in Industrial Networks Based on Structure AnalysisTianyu Wang0Peng Zeng1Jianming Zhao2Xianda Liu3Bowen Zhang4State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaIndustrial network systems are facing various new challenges, such as increasing functional failure factors, the accelerating penetration of information threats, and complex and diverse attack methods. Industrial networks are often vulnerable to natural or intentional disasters; therefore, it is highly invaluable to research to identify the influential nodes. Most of the state-of-the-art evaluates the importance of the nodes according to one or more network metrics. Moreover, there are no metrics reflecting all the properties of the network. In this paper, a novel method (Structure-based Identification Method, SIM) to identify the influential nodes in industrial networks is proposed based on the network structure, which goes beyond the use of network metrics. The SIM method extracts the weakly connected components, which are more likely to survive after the important nodes are attacked in the network. Evaluation results show that the SIM method obtains better results than the state-of-the-art methods to identify influential nodes in real-world industrial networks and has a good prospect to be applied in industrial application.https://www.mdpi.com/2073-8994/14/2/211industrial networksinfluential nodesstructure-based identification method |
| spellingShingle | Tianyu Wang Peng Zeng Jianming Zhao Xianda Liu Bowen Zhang Identification of Influential Nodes in Industrial Networks Based on Structure Analysis Symmetry industrial networks influential nodes structure-based identification method |
| title | Identification of Influential Nodes in Industrial Networks Based on Structure Analysis |
| title_full | Identification of Influential Nodes in Industrial Networks Based on Structure Analysis |
| title_fullStr | Identification of Influential Nodes in Industrial Networks Based on Structure Analysis |
| title_full_unstemmed | Identification of Influential Nodes in Industrial Networks Based on Structure Analysis |
| title_short | Identification of Influential Nodes in Industrial Networks Based on Structure Analysis |
| title_sort | identification of influential nodes in industrial networks based on structure analysis |
| topic | industrial networks influential nodes structure-based identification method |
| url | https://www.mdpi.com/2073-8994/14/2/211 |
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