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
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    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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303821
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