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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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 27; no. 4; p. 406
Main Authors: Tan, Fu, Chen, Xiaolong, Chen, Rui, Wang, Ruijie, Huang, Chi, Cai, Shimin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 10.04.2025
MDPI
Subjects:
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
United Kingdom
China
influential node identification
complex network
visibility graph algorithm
multi-attribute features
Dempster–Shafer evidence theory
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary: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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ISSN:1099-4300
1099-4300
DOI:10.3390/e27040406