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
Show more…
    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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/302197
Statistics

Document views: 6 File downloads:
  • zho_iin.pdf: 1