Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information
Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely appl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ad0e89 |
| _version_ | 1797435553190248448 |
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| author | Hao Wang Jian Wang Qian Liu Shuang-ping Yang Jun-jie Wen Na Zhao |
| author_facet | Hao Wang Jian Wang Qian Liu Shuang-ping Yang Jun-jie Wen Na Zhao |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations. |
| first_indexed | 2024-03-09T10:49:24Z |
| format | Article |
| id | doaj.art-dbf8d045f45148a4926d12fd8722e282 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-09T10:49:24Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-dbf8d045f45148a4926d12fd8722e2822023-12-01T08:48:28ZengIOP PublishingNew Journal of Physics1367-26302023-01-01251212300510.1088/1367-2630/ad0e89Identifying key spreaders in complex networks based on local clustering coefficient and structural hole informationHao Wang0Jian Wang1Qian Liu2https://orcid.org/0000-0002-3252-2845Shuang-ping Yang3Jun-jie Wen4Na Zhao5Key Laboratory in Software Engineering of Yunnan Province, Yunnan University , Kunming, Yunnan 650091, People’s Republic of ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology , Kunming, Yunnan 650504, People’s Republic of ChinaSchool of Economics and Management, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055, People’s Republic of ChinaKey Laboratory in Software Engineering of Yunnan Province, Yunnan University , Kunming, Yunnan 650091, People’s Republic of ChinaInnovation & Digital Center, Yunnan Tin Industry Group (Holdings) Co , Kunming, Yunnan 650000, People’s Republic of ChinaKey Laboratory in Software Engineering of Yunnan Province, Yunnan University , Kunming, Yunnan 650091, People’s Republic of China; The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province , Kunming, Yunnan 650201, People’s Republic of ChinaIdentifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.https://doi.org/10.1088/1367-2630/ad0e89complex networkclustering coefficientstructural holekey spreader |
| spellingShingle | Hao Wang Jian Wang Qian Liu Shuang-ping Yang Jun-jie Wen Na Zhao Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information New Journal of Physics complex network clustering coefficient structural hole key spreader |
| title | Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| title_full | Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| title_fullStr | Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| title_full_unstemmed | Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| title_short | Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| title_sort | identifying key spreaders in complex networks based on local clustering coefficient and structural hole information |
| topic | complex network clustering coefficient structural hole key spreader |
| url | https://doi.org/10.1088/1367-2630/ad0e89 |
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