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| An Effective Distance-based Measure for Node's Influence in Complex Network |
| MA Yuanyuan, HAN Hua
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| Department of Science, Wuhan University of Technology, Wuhan 430070, China |
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Abstract The measurement method of node influence based on network structure features focuses on considering the information of neighbor nodes, ignoring the difference in topology structure between nodes. To solve this problem, this paper proposes an algorithm for measuring node influence based on network structure. First, the effective distance hidden behind the network is introduced, and the importance of nodes is measured from two perspectives. Secondly, in order to overcome the influence of subjective factors in angle fusion, the influence of nodes in the complex network is calculated by using the VIKOR method in the multi-attribute decision making theory, and is sorted. In six real networks, numerical simulation experiments are carried out by using SIR model, and the ranking differentiation and accuracy are compared with other centrality algorithms. Experimental results show that the proposed method can not only obtain more accurate sorting results, but also effectively reduce the occurrence frequency of the same sorting nodes.
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Received: 19 November 2020
Published: 21 February 2022
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