Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Fan Yang (), Ruisheng Zhang, Zhao Yang (), Rongjing Hu (), Mengtian Li (), Yongna Yuan () and Keqin Li ()
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Fan Yang: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Ruisheng Zhang: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Zhao Yang: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Rongjing Hu: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Mengtian Li: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Yongna Yuan: School of Information Science and Engineering, Lanzhou University Lanzhou, Gansu 730000, P. R. China
Keqin Li: Department of Computer Science, State University of New York New Paltz, NY 12561, USA

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 01, 1-17

Abstract: Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

Keywords: Complex networks; the most influential spreaders; K-Shell decomposition; Local K-Shell Sum (LKSS); Extended Local K-Shell Sum (ELKSS) centrality; Susceptible-Infectious-Recovered (SIR) model (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0129183117500140

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