Efficient and Accurate Robustness Estimation for Large Complex Networks

TL;DR

This paper introduces a fast, scalable algorithm for estimating network robustness that maintains high accuracy, enabling efficient analysis of large complex networks.

Contribution

A novel sub-quadratic time algorithm for robustness estimation that rivals betweenness centrality in accuracy while significantly reducing computation time.

Findings

Estimates robustness close to betweenness centrality

Achieves orders of magnitude faster computation

Effective on real-world and random networks

Abstract

Robustness estimation is critical for the design and maintenance of resilient networks, one of the global challenges of the 21st century. Existing studies exploit network metrics to generate attack strategies, which simulate intentional attacks in a network, and compute a metric-induced robustness estimation. While some metrics are easy to compute, e.g. degree centrality, other, more accurate, metrics require considerable computation efforts, e.g. betweennes centrality. We propose a new algorithm for estimating the robustness of a network in sub-quadratic time, i.e., significantly faster than betweenness centrality. Experiments on real-world networks and random networks show that our algorithm estimates the robustness of networks close to or even better than betweenness centrality, while being orders of magnitudes faster. Our work contributes towards scalable, yet accurate methods for robustness estimation of large complex networks.