Exploring the Potential for Enhancing Structural Robustness of Complex Networks

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, which represents networks using extracted features; RPE-V, which provides visualization; RPE-P, which predicts network robustness enhancement potential. As a case study, the RPE is evaluated by integrating a set of 20 graph features as RPE-F, employing t-distributed stochastic neighbor embedding (t-SNE) as RPE-V, and utilizing three machine learning algorithms as RPE-P. In particular, RPE-V enables meaningful visualization for observing the robustness enhancement process, while RPE-P quantifies the robustness enhancement potential for the given network. Extensive experimental studies demonstrate that the proposed RPE outperforms two state-of-the-art CNN- and GNN-based schemes, with acceptably low prediction errors. These findings highlight the effectiveness of the RPE as a versatile tool for understanding, analyzing, and improving network robustness.

© 2025 IEEE. All rights reserved, including rights for text and data mining, and training of artificial intelligence and similar technologies.