Robustness of correlated networks against propagating attacks

TL;DR

This study examines how correlated networks respond to disease spread, revealing that they are generally more robust than uncorrelated networks unless the initial infection is very large, with robustness influenced by network structure.

Contribution

The paper introduces a numerical analysis of correlated network robustness against propagating attacks, highlighting the effects of assortativity and disassortativity on network resilience.

Findings

Correlated networks are more robust than uncorrelated ones for small initial infections.

Disassortative networks become fragile with large initial infections.

Assortative networks maintain robustness even with large initial infections.

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.