Robustness of correlated networks against propagating attacks

Abstract

We investigate robustness of correlated networks against propagating attacks modeled by a susceptible-infected-removed model. By Monte-Carlo simulations, we numerically determine the first critical infection rate, above which a global outbreak of disease occurs, and the second critical infection rate, above which disease disintegrates the network. Our result shows that correlated networks are robust compared to the uncorrelated ones, regardless of whether they are assortative or disassortative, when a fraction of infected nodes in an initial state is not too large. For large initial fraction, disassortative network becomes fragile while assortative network holds robustness. This behavior is related to the layered network structure inevitably generated by a rewiring procedure we adopt to realize correlated networks.

6 pages, 13 figures

References in corpus (7)

• Critical phenomena in complex networks
• Threshold effects for two pathogens spreading on a network
• Percolation on correlated networks
• Size of Outbreaks Near the Epidemic Threshold
• Cavity analysis on the robustness of random networks against targeted attacks: Influences of degree-degree correlations
• Monte-Carlo simulation study of the two-stage percolation transition in enhanced binary trees
• Solution of an infection model near threshold