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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| Vydáno v: | Entropy (Basel, Switzerland) Ročník 27; číslo 4; s. 406 |
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10.04.2025
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Tan, Fu Chen, Xiaolong Cai, Shimin Chen, Rui Wang, Ruijie Huang, Chi |
| AuthorAffiliation | 1 School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China; ftan5967@gmail.com 3 Engineering Research Center of Intelligent Finance, Ministry of Education, Chengdu 611130, China 5 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China; shimin.cai81@gmail.com 4 School of Mathematics, Aba Teachers College, Wenchuan 623002, China 2 School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China; chenxiaolong@swufe.edu.cn (X.C.); chenrui062@gmail.com (R.C.); huangchi@swufe.edu.cn (C.H.) |
| AuthorAffiliation_xml | – name: 3 Engineering Research Center of Intelligent Finance, Ministry of Education, Chengdu 611130, China – name: 5 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China; shimin.cai81@gmail.com – name: 2 School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China; chenxiaolong@swufe.edu.cn (X.C.); chenrui062@gmail.com (R.C.); huangchi@swufe.edu.cn (C.H.) – name: 4 School of Mathematics, Aba Teachers College, Wenchuan 623002, China – name: 1 School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China; ftan5967@gmail.com |
| Author_xml | – sequence: 1 givenname: Fu surname: Tan fullname: Tan, Fu – sequence: 2 givenname: Xiaolong surname: Chen fullname: Chen, Xiaolong – sequence: 3 givenname: Rui surname: Chen fullname: Chen, Rui – sequence: 4 givenname: Ruijie surname: Wang fullname: Wang, Ruijie – sequence: 5 givenname: Chi surname: Huang fullname: Huang, Chi – sequence: 6 givenname: Shimin orcidid: 0000-0002-2089-5150 surname: Cai fullname: Cai, Shimin |
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| Keywords | influential node identification complex network visibility graph algorithm multi-attribute features Dempster–Shafer evidence theory |
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| SubjectTerms | Accuracy Algorithms Analysis complex network Complexity COVID-19 vaccines Decision-making Dempster-Shafer Method Dempster–Shafer evidence theory Disease transmission Disintegration Effectiveness Financial institutions Graphs influential node identification Information sources Methods multi-attribute features Networks Neural networks Nodes Pandemics Propagation Reliability Social networks Time series Uncertainty visibility graph algorithm |
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| Title | Identifying Influential Nodes Based on Evidence Theory in Complex Network |
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