Combinatorial Trace Method for Network Immunization

TL;DR

This paper introduces a novel combinatorial trace method and an efficient approximation algorithm for network immunization, significantly improving runtime and effectiveness over existing solutions in real-world networks.

Contribution

It establishes a new relationship between network vulnerability measures and combinatorial properties, leading to a scalable approximation algorithm with theoretical guarantees.

Findings

Algorithm outperforms state-of-the-art solutions by an order of magnitude.

Runtime is significantly lower in practice compared to existing methods.

Demonstrated effectiveness on various real-world networks.

Abstract

Immunizing a subset of nodes in a network - enabling them to identify and withstand the spread of harmful content - is one of the most effective ways to counter the spread of malicious content. It has applications in network security, public health policy, and social media surveillance. Finding a subset of nodes whose immunization results in the least vulnerability of the network is a computationally challenging task. In this work, we establish a relationship between a widely used network vulnerability measure and the combinatorial properties of networks. Using this relationship and graph summarization techniques, we propose an efficient approximation algorithm to find a set of nodes to immunize. We provide theoretical justifications for the proposed solution and analytical bounds on the runtime of our algorithm. We empirically demonstrate on various real-world networks that the performance of our algorithm is an order of magnitude better than the state of the art solution. We also show that in practice the runtime of our algorithm is significantly lower than that of the best-known solution.