Identifying influential nodes in complex networks based on global and local structure

Jinfang Sheng, Jinying Dai, Bin Wang, Guihua Duan, Jun Long, Junkai Zhang, Kerong Guan, Sheng Hu, Long Chen and Wanghao Guan

Physica A: Statistical Mechanics and its Applications, 2020, vol. 541, issue C

Abstract: Identifying influential nodes in complex networks is still an open issue. A number of measures have been proposed to improve the validity and accuracy of the influential nodes in complex networks. In this paper, we propose a new method, called GLS, to identify influential nodes. This method aims to determine the influence of the nodes themselves, while combining the structural characteristics of the network. This method considers not only the local structure of the network but also its global structure. The influence of the global structure is measured by its closeness to all other nodes in the network, but the influence of local structures only considers the influence contribution of the nearest neighbor nodes. To evaluate the performance of GLS, we use the Susceptible-Infected-Recovered (SIR) model to examine the spreading efficiency of each node, and compare GLS with PageRank, Hyperlink Induced Topic Search (Hits), K-shell, H-index, eigenvector centrality (EC), closeness centrality (CC), ProfitLeader, betweenness centrality (BC) and Weighted Formal Concept Analysis (WFCA) on 8 real-world networks. The experimental results show that GLS can rank the spreading ability of nodes more accurately and more efficiently than other methods.

Keywords: Influential nodes; Complex networks; Global structure; Local structure; Neighbor contribution (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318308

DOI: 10.1016/j.physa.2019.123262

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