Identifying Influential Nodes Based on Evidence Theory in Complex Network
Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real net...
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MDPI AG
2025-04-01
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| author | Fu Tan Xiaolong Chen Rui Chen Ruijie Wang Chi Huang Shimin Cai |
| author_facet | Fu Tan Xiaolong Chen Rui Chen Ruijie Wang Chi Huang Shimin Cai |
| author_sort | Fu Tan |
| collection | DOAJ |
| description | Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm. |
| format | Article |
| id | doaj-art-85191bda46604d269a67d84f693c71b6 |
| institution | OA Journals |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-85191bda46604d269a67d84f693c71b62025-08-20T02:28:14ZengMDPI AGEntropy1099-43002025-04-0127440610.3390/e27040406Identifying Influential Nodes Based on Evidence Theory in Complex NetworkFu Tan0Xiaolong Chen1Rui Chen2Ruijie Wang3Chi Huang4Shimin Cai5School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaSchool of Mathematics, Aba Teachers College, Wenchuan 623002, ChinaSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, ChinaBig Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, ChinaInfluential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm.https://www.mdpi.com/1099-4300/27/4/406complex networkinfluential node identificationmulti-attribute featuresDempster–Shafer evidence theoryvisibility graph algorithm |
| spellingShingle | Fu Tan Xiaolong Chen Rui Chen Ruijie Wang Chi Huang Shimin Cai Identifying Influential Nodes Based on Evidence Theory in Complex Network Entropy complex network influential node identification multi-attribute features Dempster–Shafer evidence theory visibility graph algorithm |
| title | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
| title_full | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
| title_fullStr | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
| title_full_unstemmed | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
| title_short | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
| title_sort | identifying influential nodes based on evidence theory in complex network |
| topic | complex network influential node identification multi-attribute features Dempster–Shafer evidence theory visibility graph algorithm |
| url | https://www.mdpi.com/1099-4300/27/4/406 |
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