Identifying Influential Nodes of Complex Networks Based on Trust-Value
The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/13/11/280 |
| _version_ | 1797548840731017216 |
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| author | Jinfang Sheng Jiafu Zhu Yayun Wang Bin Wang Zheng’ang Hou |
| author_facet | Jinfang Sheng Jiafu Zhu Yayun Wang Bin Wang Zheng’ang Hou |
| author_sort | Jinfang Sheng |
| collection | DOAJ |
| description | The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks. |
| first_indexed | 2024-03-10T15:05:29Z |
| format | Article |
| id | doaj.art-d0823cc57f074dc3945edb43f58426f3 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T15:05:29Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d0823cc57f074dc3945edb43f58426f32023-11-20T19:50:13ZengMDPI AGAlgorithms1999-48932020-11-01131128010.3390/a13110280Identifying Influential Nodes of Complex Networks Based on Trust-ValueJinfang Sheng0Jiafu Zhu1Yayun Wang2Bin Wang3Zheng’ang Hou4School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaThe real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.https://www.mdpi.com/1999-4893/13/11/280influential nodescomplex networkPageRankrtust-value |
| spellingShingle | Jinfang Sheng Jiafu Zhu Yayun Wang Bin Wang Zheng’ang Hou Identifying Influential Nodes of Complex Networks Based on Trust-Value Algorithms influential nodes complex network PageRank rtust-value |
| title | Identifying Influential Nodes of Complex Networks Based on Trust-Value |
| title_full | Identifying Influential Nodes of Complex Networks Based on Trust-Value |
| title_fullStr | Identifying Influential Nodes of Complex Networks Based on Trust-Value |
| title_full_unstemmed | Identifying Influential Nodes of Complex Networks Based on Trust-Value |
| title_short | Identifying Influential Nodes of Complex Networks Based on Trust-Value |
| title_sort | identifying influential nodes of complex networks based on trust value |
| topic | influential nodes complex network PageRank rtust-value |
| url | https://www.mdpi.com/1999-4893/13/11/280 |
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