Recoverable prevalence in growing scale-free networks and the effective immunization

TL;DR

This paper investigates how computer viruses persist or die out in growing scale-free networks, using simulations and bifurcation analysis to understand effective immunization strategies, especially targeting hubs.

Contribution

It introduces a realistic model for virus spread in growing scale-free networks and derives conditions for extinction through immunization strategies using mean-field analysis.

Findings

Viruses can persist or be eradicated depending on immunization strategies.

Targeted immunization of hubs is effective in controlling virus spread.

The model aligns qualitatively with real data on network growth.

Abstract

We study the persistent recoverable prevalence and the extinction of computer viruses via e-mails on a growing scale-free network with new users, which structure is estimated form real data. The typical phenomenon is simulated in a realistic model with the probabilistic execution and detection of viruses. Moreover, the conditions of extinction by random and targeted immunizations for hubs are derived through bifurcation analysis for simpler models by using a mean-field approximation without the connectivity correlations. We can qualitatively understand the mechanisms of the spread in linearly growing scale-free networks.