Robustness of networks against propagating attacks under vaccination strategies

Takehisa Hasegawa, Naoki Masuda

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


We study the effect of vaccination on the robustness of networks against propagating attacks that obey the susceptible-infected-removed model. By extending the generating function formalism developed by Newman (2005Phys.Rev.Lett.95108701), we analytically determine the robustness of networks that depends on the vaccination parameters. We consider the random defense where nodes are vaccinated randomly and the degree-based defense where hubs are preferentially vaccinated. We show that, when vaccines are inefficient, the random graph is more robust against propagating attacks than the scale-free network. When vaccines are relatively efficient, the scale-free network with the degree-based defense is more robust than the random graph with the random defense and the scale-free network with the random defense.

Original languageEnglish
Article numberP09014
JournalJournal of Statistical Mechanics: Theory and Experiment
Issue number9
Publication statusPublished - 2011 Sept


  • communication
  • network dynamics
  • robust and stochastic optimization
  • supply and information networks

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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