Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions
IntroductionIdentifying influential spreaders in complex networks is crucial for understanding information propagation and disease immunity. The spreading ability of a node has been commonly assessed through its neighbor information. However, current methods do not provide specific explanations for...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1529904/full |
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| author | Weiwei Zhu Xuchen Meng Jiaye Sheng Dayong Zhang |
| author_facet | Weiwei Zhu Xuchen Meng Jiaye Sheng Dayong Zhang |
| author_sort | Weiwei Zhu |
| collection | DOAJ |
| description | IntroductionIdentifying influential spreaders in complex networks is crucial for understanding information propagation and disease immunity. The spreading ability of a node has been commonly assessed through its neighbor information. However, current methods do not provide specific explanations for the role of neighbors or distinguish their individual contributions to the spread of information.MethodsTo address these limitations, we propose an efficient ranking algorithm that strictly distinguishes the contribution of each neighbor in information spreading. This method combines the count of common neighbors with the K-shell value of each node to produce its ranking. By integrating these two factors, our approach aims to offer a more precise measure of a node's influence within a network.ResultsExtensive experiments were conducted using Kendall’s rank correlation, monotonicity tests, and the Susceptible-Infected-Recovered (SIR) epidemic model on real-world networks. These tests demonstrated the effectiveness of our proposed algorithm in identifying influential spreaders accurately.DiscussionFurthermore, computational complexity analysis indicates that our algorithm consumes less time compared to existing methods, suggesting it can be efficiently applied to large-scale networks. |
| format | Article |
| id | doaj-art-6e639d7f901d47f282f7a3e7c809e1f0 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-6e639d7f901d47f282f7a3e7c809e1f02025-01-27T05:14:39ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-01-011310.3389/fphy.2025.15299041529904Identifying vital spreaders in large-scale networks based on neighbor multilayer contributionsWeiwei Zhu0Xuchen Meng1Jiaye Sheng2Dayong Zhang3School of Economics and Management, University of Chinese Academy of Sciences, Beijing, ChinaCollaborative Innovation Center for Computational Social Science, Harbin Institute of Technology, Harbin, ChinaCollaborative Innovation Center for Computational Social Science, Harbin Institute of Technology, Harbin, ChinaCollaborative Innovation Center for Computational Social Science, Harbin Institute of Technology, Harbin, ChinaIntroductionIdentifying influential spreaders in complex networks is crucial for understanding information propagation and disease immunity. The spreading ability of a node has been commonly assessed through its neighbor information. However, current methods do not provide specific explanations for the role of neighbors or distinguish their individual contributions to the spread of information.MethodsTo address these limitations, we propose an efficient ranking algorithm that strictly distinguishes the contribution of each neighbor in information spreading. This method combines the count of common neighbors with the K-shell value of each node to produce its ranking. By integrating these two factors, our approach aims to offer a more precise measure of a node's influence within a network.ResultsExtensive experiments were conducted using Kendall’s rank correlation, monotonicity tests, and the Susceptible-Infected-Recovered (SIR) epidemic model on real-world networks. These tests demonstrated the effectiveness of our proposed algorithm in identifying influential spreaders accurately.DiscussionFurthermore, computational complexity analysis indicates that our algorithm consumes less time compared to existing methods, suggesting it can be efficiently applied to large-scale networks.https://www.frontiersin.org/articles/10.3389/fphy.2025.1529904/fulllarge-scale networkrankinig methodvital spreaderscommon neighborsSIR epidemic model |
| spellingShingle | Weiwei Zhu Xuchen Meng Jiaye Sheng Dayong Zhang Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions Frontiers in Physics large-scale network rankinig method vital spreaders common neighbors SIR epidemic model |
| title | Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions |
| title_full | Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions |
| title_fullStr | Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions |
| title_full_unstemmed | Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions |
| title_short | Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions |
| title_sort | identifying vital spreaders in large scale networks based on neighbor multilayer contributions |
| topic | large-scale network rankinig method vital spreaders common neighbors SIR epidemic model |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1529904/full |
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