A Quick Framework for Evaluating Worst Robustness of Complex Networks

TL;DR

This paper introduces a new framework using Most Destruction Attack and an adapted CNN to efficiently evaluate the worst-case robustness of complex networks, addressing limitations of traditional simulation-based methods.

Contribution

It proposes the concept of Worst Robustness and develops a rapid prediction method using CNN, improving assessment accuracy and efficiency for various network types.

Findings

MDA effectively assesses worst robustness in diverse networks

Adapted CNN predicts worst robustness with high accuracy

Framework reduces evaluation time compared to simulation attacks

Abstract

Robustness is pivotal for comprehending, designing, optimizing, and rehabilitating networks, with simulation attacks being the prevailing evaluation method. Simulation attacks are often time-consuming or even impractical, however, a more crucial yet persistently overlooked drawback is that any attack strategy merely provides a potential paradigm of disintegration. The key concern is: in the worst-case scenario or facing the most severe attacks, what is the limit of robustness, referred to as ``Worst Robustness'', for a given system? Understanding a system's worst robustness is imperative for grasping its reliability limits, accurately evaluating protective capabilities, and determining associated design and security maintenance costs. To address these challenges, we introduce the concept of Most Destruction Attack (MDA) based on the idea of knowledge stacking. MDA is employed to assess the worst robustness of networks, followed by the application of an adapted CNN algorithm for rapid worst robustness prediction. We establish the logical validity of MDA and highlight the exceptional performance of the adapted CNN algorithm in predicting the worst robustness across diverse network topologies, encompassing both model and empirical networks.