Excavating important nodes in complex networks based on the heat conduction model
Abstract Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excav...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-58320-3 |
| _version_ | 1797219792272228352 |
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| author | Haifeng Hu Junhui Zheng Wentao Hu Feifei Wang Guan Wang Jiangwei Zhao Liugen Wang |
| author_facet | Haifeng Hu Junhui Zheng Wentao Hu Feifei Wang Guan Wang Jiangwei Zhao Liugen Wang |
| author_sort | Haifeng Hu |
| collection | DOAJ |
| description | Abstract Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes. |
| first_indexed | 2024-04-24T12:39:16Z |
| format | Article |
| id | doaj.art-4e49aa5f20614328936d06c92b930f84 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T12:39:16Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4e49aa5f20614328936d06c92b930f842024-04-07T11:17:56ZengNature PortfolioScientific Reports2045-23222024-04-0114111610.1038/s41598-024-58320-3Excavating important nodes in complex networks based on the heat conduction modelHaifeng Hu0Junhui Zheng1Wentao Hu2Feifei Wang3Guan Wang4Jiangwei Zhao5Liugen Wang6Pingdingshan UniversityPingdingshan UniversityChina PingMei ShenMa GroupPingdingshan UniversityPingdingshan UniversityPingdingshan UniversityChina PingMei ShenMa GroupAbstract Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.https://doi.org/10.1038/s41598-024-58320-3Heat conduction modelDegree densityNetwork densityDistanceSIR modelIC model |
| spellingShingle | Haifeng Hu Junhui Zheng Wentao Hu Feifei Wang Guan Wang Jiangwei Zhao Liugen Wang Excavating important nodes in complex networks based on the heat conduction model Scientific Reports Heat conduction model Degree density Network density Distance SIR model IC model |
| title | Excavating important nodes in complex networks based on the heat conduction model |
| title_full | Excavating important nodes in complex networks based on the heat conduction model |
| title_fullStr | Excavating important nodes in complex networks based on the heat conduction model |
| title_full_unstemmed | Excavating important nodes in complex networks based on the heat conduction model |
| title_short | Excavating important nodes in complex networks based on the heat conduction model |
| title_sort | excavating important nodes in complex networks based on the heat conduction model |
| topic | Heat conduction model Degree density Network density Distance SIR model IC model |
| url | https://doi.org/10.1038/s41598-024-58320-3 |
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