# Smart Rewiring for Network Robustness

arXiv: https://arxiv.org/abs/1303.5269 · HTML: https://mlsanity.com/paper/1303.5269/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2013.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A Learning Convolutional Neural Network Approach for Network Robustness Prediction](https://mlsanity.com/paper/2203.10552/) (2022)
- [A Graph Transformer-Driven Approach for Network Robustness Learning](https://mlsanity.com/paper/2306.06913/) (2023)
- [AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models](https://mlsanity.com/paper/2410.17656/) (2024)

## Abstract (arXiv)

While new forms of attacks are developed every day to compromise essential infrastructures, service providers are also expected to develop strategies to mitigate the risk of extreme failures. In this context, tools of Network Science have been used to evaluate network robustness and propose resilient topologies against attacks. We present here a new rewiring method to modify the network topology improving its robustness, based on the evolution of the network largest component during a sequence of targeted attacks. In comparison to previous strategies, our method lowers by several orders of magnitude the computational effort necessary to improve robustness. Our rewiring also drives the formation of layers of nodes with similar degree while keeping a highly modular structure. This "modular onion-like structure" is a particular class of the onion-like structure previously described in the literature. We apply our rewiring strategy to an unweighted representation of the World Air Transportation network and show that an improvement of 30% in its overall robustness can be achieved through smart swaps of around 9% of its links.