Identify influential nodes in complex networks: A k-orders entropy-based method

Yali Wu, Ang Dong, Yuanguang Ren and Qiaoyong Jiang

Physica A: Statistical Mechanics and its Applications, 2023, vol. 632, issue P1

Abstract: Identifying influential nodes is a recognized challenge for the tremendous number of nodes in complex networks. Most of proposed methods detect the influential nodes based on their degree or topological location, which only consider the local or global information of the network causing inaccuracy. In this paper, we propose a k-orders entropy-based method to identify influential nodes. The influence of node is determined by its entropy with local and global information. The entropy reflecting local information is measured by the different order neighbors’ information of nodes while the entropy reflecting global information by the betweenness centrality. The experiments conducted on real-world networks demonstrate the proposed method is more accurate than other methods.

Keywords: Complex networks; Influential nodes; Entropy; Local information; Global information (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:632:y:2023:i:p1:s0378437123008579

DOI: 10.1016/j.physa.2023.129302

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