# Robustness Potential Explorer (RPE)

This repository contains the source code for the paper:

**Yang Lou, Chengpei Wu, Liang Chen, Wenli Huang, Lei Zhou, Lin Wang, and Guanrong Chen**,
*"Exploring the Potential for Enhancing Structural Robustness of Complex Networks"*,
IEEE Computational Intelligence Magazine, vol. 20, no. 4, pp. 83–98, 2025.
DOI: [10.1109/MCI.2025.3599463](https://doi.org/10.1109/MCI.2025.3599463)

Network robustness is vital in both engineering and social systems to maintain reliability, resilience, and security against various disruptions such as malicious attacks, random failures, and cascading failures. This paper presents a framework called the **Robustness Potential Explorer (RPE)** to facilitate the study of network robustness. The RPE framework comprises three components:

- **RPE-F**: Feature extraction from networks
- **RPE-V**: Visualization using t-SNE
- **RPE-P**: Prediction of robustness enhancement potential using machine learning

Extensive experiments show that RPE outperforms CNN- and GNN-based methods with low prediction errors.

📄 License
This code is released under the MIT License. See LICENSE file for details.

🙋 Contact
For questions or collaboration, please contact:
**Dr. Chengpei Wu**: chengpei.wu@hotmail.com
**Mr. Liang Chen**: chen.liang.cl@outlook.com

📚 Citation
@article{lou2025ieeecim,
  title={Exploring the Potential for Enhancing Structural Robustness of Complex Networks},
  author={Yang Lou and Chengpei Wu and Liang Chen and Wenli Huang and Lei Zhou and Lin Wang and Guanrong Chen},
  journal={IEEE Computational Intelligence Magazine},
  volume={20},
  number={4},
  pages={83--98},
  year={2025},
  doi={10.1109/MCI.2025.3599463}
