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Identifying influential nodes in complex networks

Author

Listed:
  • Chen, Duanbing
  • Lü, Linyuan
  • Shang, Ming-Sheng
  • Zhang, Yi-Cheng
  • Zhou, Tao

Abstract

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.

Suggested Citation

  • Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1777-1787
    DOI: 10.1016/j.physa.2011.09.017
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Hu, Hai-Bo & Wang, Xiao-Fan, 2008. "Unified index to quantifying heterogeneity of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3769-3780.
    3. Dangalchev, Chavdar, 2006. "Residual closeness in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 365(2), pages 556-564.
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