Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model
The research into complex networks has consistently attracted significant attention, with the identification of important nodes within these networks being one of the central challenges in this field of study. Existing methods for identifying key nodes based on effective distance commonly suffer fro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/4/408 |
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| author | Sheng Zhang Fuhao Liu Yuyuan Huang Ziqiang Luo Ka Sun Hongmei Mao |
| author_facet | Sheng Zhang Fuhao Liu Yuyuan Huang Ziqiang Luo Ka Sun Hongmei Mao |
| author_sort | Sheng Zhang |
| collection | DOAJ |
| description | The research into complex networks has consistently attracted significant attention, with the identification of important nodes within these networks being one of the central challenges in this field of study. Existing methods for identifying key nodes based on effective distance commonly suffer from high time complexity and often overlook the impact of nodes’ multi-attribute characteristics on the identification outcomes. To identify important nodes in complex networks more efficiently and accurately, we propose a novel method that leverages an improved effective distance fusion model to identify important nodes. This method effectively reduces redundant calculations of effective distances by employing an effective-influence node set. Furthermore, it incorporates the multi-attribute characteristics of the nodes, characterizing their propagation capabilities by considering local, global, positional, and clustering information and thereby providing a more comprehensive assessment of node importance within complex networks. |
| format | Article |
| id | doaj-art-6d06cab6d7484b1497af458fd6d0073f |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-6d06cab6d7484b1497af458fd6d0073f2025-08-20T03:13:54ZengMDPI AGEntropy1099-43002025-04-0127440810.3390/e27040408Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity ModelSheng Zhang0Fuhao Liu1Yuyuan Huang2Ziqiang Luo3Ka Sun4Hongmei Mao5School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang 330063, ChinaThe research into complex networks has consistently attracted significant attention, with the identification of important nodes within these networks being one of the central challenges in this field of study. Existing methods for identifying key nodes based on effective distance commonly suffer from high time complexity and often overlook the impact of nodes’ multi-attribute characteristics on the identification outcomes. To identify important nodes in complex networks more efficiently and accurately, we propose a novel method that leverages an improved effective distance fusion model to identify important nodes. This method effectively reduces redundant calculations of effective distances by employing an effective-influence node set. Furthermore, it incorporates the multi-attribute characteristics of the nodes, characterizing their propagation capabilities by considering local, global, positional, and clustering information and thereby providing a more comprehensive assessment of node importance within complex networks.https://www.mdpi.com/1099-4300/27/4/408complex networksnode importanceeffective distancefusion gravity |
| spellingShingle | Sheng Zhang Fuhao Liu Yuyuan Huang Ziqiang Luo Ka Sun Hongmei Mao Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model Entropy complex networks node importance effective distance fusion gravity |
| title | Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model |
| title_full | Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model |
| title_fullStr | Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model |
| title_full_unstemmed | Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model |
| title_short | Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model |
| title_sort | identification of important nodes based on local effective distance integrated gravity model |
| topic | complex networks node importance effective distance fusion gravity |
| url | https://www.mdpi.com/1099-4300/27/4/408 |
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