Identifying influential spreaders by weighted LeaderRank

Li, Qian; Zhou, Tao; Lü, Linyuan; Chen, Duanbing

doi:10.1016/j.physa.2014.02.041

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Journal article

Identifying influential spreaders by weighted LeaderRank

Li, Qian Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Big Data Lab, Cloud Valley, Beijing, China - College of Physics and Technology, Guangxi Normal University, Guilin, China
Zhou, Tao Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China
Lü, Linyuan Institute of Information Economy, Alibaba Business College, Hangzhou Normal University, China - Department of Physics, University of Fribourg, Switzerland
Chen, Duanbing Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China

15.06.2014

Published in:

Physica A: Statistical Mechanics and its Applications. - 2014, vol. 404, p. 47–55

English Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (Lü et al., 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders; (ii) the higher tolerance to noisy data; and (iii) the higher robustness to intentional attacks.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language