Towards identifying influential nodes in complex networks using semi-local centrality metrics
The influence of the node refers to the ability of the node to disseminate information. The faster and wider the node spreads, the greater its influence. There are many classical topological metrics that can be used to evaluate the influencing ability of nodes. Degree centrality, betweenness central...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915782300352X |
| _version_ | 1797389520994304000 |
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| author | Kun Zhang Yu Zhou Haixia Long Chaoyang Wang Haizhuang Hong Seyed Mostafa Armaghan |
| author_facet | Kun Zhang Yu Zhou Haixia Long Chaoyang Wang Haizhuang Hong Seyed Mostafa Armaghan |
| author_sort | Kun Zhang |
| collection | DOAJ |
| description | The influence of the node refers to the ability of the node to disseminate information. The faster and wider the node spreads, the greater its influence. There are many classical topological metrics that can be used to evaluate the influencing ability of nodes. Degree centrality, betweenness centrality, closeness centrality and local centrality are among the most common metrics for identifying influential nodes in complex networks. Degree centrality is very simple but not very effective. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but they are not compatible on large-scale networks due to their high complexity. In order to design a ranking method of influential nodes, in this paper a new semi-local centrality metric is proposed based on the relative change in the average shortest path of the entire network. Meanwhile, our metric provides a quantitative global importance model to measure the overall influence of each node. To evaluate the performance of the proposed centrality metric, we use the Susceptible-Infected-Recovered (SIR) epidemic model. Experimental results on several real-world networks show that the proposed metric has competitive performance in identifying influential nodes with existing equivalent centrality metrics and has high efficiency in dealing with large-scale networks. The effectiveness of the proposed metric has been proven with numerical examples and Kendall's coefficient. |
| first_indexed | 2024-03-08T22:57:11Z |
| format | Article |
| id | doaj.art-145d5dc93b784aee953e463c933d8a0c |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-08T22:57:11Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-145d5dc93b784aee953e463c933d8a0c2023-12-16T06:06:01ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-12-013510101798Towards identifying influential nodes in complex networks using semi-local centrality metricsKun Zhang0Yu Zhou1Haixia Long2Chaoyang Wang3Haizhuang Hong4Seyed Mostafa Armaghan5School of Information Science and Technology, Hainan Normal University, Haikou 571158, Hainan, China; Corresponding authors.School of Information Science and Technology, Hainan Normal University, Haikou 571158, Hainan, China; Corresponding authors.School of Information Science and Technology, Hainan Normal University, Haikou 571158, Hainan, China; Corresponding authors.CETC Guohaixintong Technology (Hainan) Co., Ltd, Sansha 570203, Hainan, ChinaCETC Guohaixintong Technology (Hainan) Co., Ltd, Sansha 570203, Hainan, ChinaDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, IranThe influence of the node refers to the ability of the node to disseminate information. The faster and wider the node spreads, the greater its influence. There are many classical topological metrics that can be used to evaluate the influencing ability of nodes. Degree centrality, betweenness centrality, closeness centrality and local centrality are among the most common metrics for identifying influential nodes in complex networks. Degree centrality is very simple but not very effective. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but they are not compatible on large-scale networks due to their high complexity. In order to design a ranking method of influential nodes, in this paper a new semi-local centrality metric is proposed based on the relative change in the average shortest path of the entire network. Meanwhile, our metric provides a quantitative global importance model to measure the overall influence of each node. To evaluate the performance of the proposed centrality metric, we use the Susceptible-Infected-Recovered (SIR) epidemic model. Experimental results on several real-world networks show that the proposed metric has competitive performance in identifying influential nodes with existing equivalent centrality metrics and has high efficiency in dealing with large-scale networks. The effectiveness of the proposed metric has been proven with numerical examples and Kendall's coefficient.http://www.sciencedirect.com/science/article/pii/S131915782300352XComplex networksInfluential nodesCentrality metricsSemi-local centralityAverage shortest path |
| spellingShingle | Kun Zhang Yu Zhou Haixia Long Chaoyang Wang Haizhuang Hong Seyed Mostafa Armaghan Towards identifying influential nodes in complex networks using semi-local centrality metrics Journal of King Saud University: Computer and Information Sciences Complex networks Influential nodes Centrality metrics Semi-local centrality Average shortest path |
| title | Towards identifying influential nodes in complex networks using semi-local centrality metrics |
| title_full | Towards identifying influential nodes in complex networks using semi-local centrality metrics |
| title_fullStr | Towards identifying influential nodes in complex networks using semi-local centrality metrics |
| title_full_unstemmed | Towards identifying influential nodes in complex networks using semi-local centrality metrics |
| title_short | Towards identifying influential nodes in complex networks using semi-local centrality metrics |
| title_sort | towards identifying influential nodes in complex networks using semi local centrality metrics |
| topic | Complex networks Influential nodes Centrality metrics Semi-local centrality Average shortest path |
| url | http://www.sciencedirect.com/science/article/pii/S131915782300352X |
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