Inverse targeting -- an effective immunization strategy

TL;DR

This paper introduces an inverse targeting immunization strategy that efficiently identifies and immunizes the most critical nodes in networks, outperforming traditional methods in both model and real-world networks.

Contribution

The paper presents a novel immunization method based on inverse targeting that improves efficiency over existing strategies in various network types.

Findings

Up to 14% more efficient in model networks.

Up to 33% more efficient in real networks.

Numerically efficient for large systems.

Abstract

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.