Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 541, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physa.2019.123262
Keywords
Influential nodes; Complex networks; Global structure; Local structure; Neighbor contribution
Categories
Funding
- National Science and Technology Major Project of China [2017ZX06002005]
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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 GIS, 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. (C) 2019 Elsevier B.V. All rights reserved.
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