Identifying influential nodes in complex networks based on expansion factor

Dong Liu, Yun Jing () and Baofang Chang ()
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Dong Liu: School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China†Engineering Technology Research Center for Computing Intelligence and Data Mining, Henan Province, Xinxiang 453007, P. R. China
Yun Jing: School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China
Baofang Chang: School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China

International Journal of Modern Physics C (IJMPC), 2016, vol. 27, issue 09, 1-16

Abstract: Identifying the top influential spreaders in a network has practical significance. In this paper, we propose a novel centrality to identify influential spreaders based on expansion factor. Nodes with high expansion factor centrality (EFC) have strong spreading capability. During the course of the work, an improved strategy is proposed to reduce the time complexity of EFC. We discuss the correlations between EFC and the other five classical indicators. Simulation results on the Susceptible-Infected-Removed (SIR) model manifest that EFC can identify influential nodes and find some critical influential nodes neglected by other indicators.

Keywords: Influential spreaders; centrality; expansion contribution; complex networks (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0129183116501059

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