Identifying influential nodes in complex networks

Lü, Linyuan; Shang, Ming-Sheng; Zhang, Yi-Cheng; Zhou, Tao

doi:10.1016/j.physa.2011.09.017

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Journal article

Identifying influential nodes in complex networks

Lü, Linyuan Physics Department, University of Fribourg, Switzerland
Shang, Ming-Sheng Web Sciences Center, University of Electronic Science and Technolog, Chengdu, China
Zhang, Yi-Cheng Web Sciences Center, University of Electronic Science and Technolog, Chengdu, China - Physics Department, University of Fribourg, Switzerland
Zhou, Tao Web Sciences Center, University of Electronic Science and Technolog, Chengdu, China - Department of Modern Physics, University of Science and Technology, Chengdu, China

02.10.2011

Published in:

Physica A: Statistical Mechanics and its Applications. - 2011, vol. 391, no. 4, p. 1777–1787

English Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language