A Graph Transformer-Driven Approach for Network Robustness Learning

TL;DR

This paper introduces NRL-GT, a graph transformer-based method that efficiently learns network robustness metrics, replacing time-consuming attack simulations and enabling rapid analysis of large-scale networks.

Contribution

The paper presents a unified, versatile graph transformer framework for learning multiple aspects of network robustness with high accuracy and efficiency, outperforming existing methods.

Findings

NRL-GT demonstrates strong generalization across different data distributions.

It achieves superior accuracy and speed compared to state-of-the-art methods.

The backbone can be transferred to other network analysis tasks.

Abstract

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks. Traditionally, network robustness is determined by attack simulations, which is very time-consuming and even incapable for large-scale networks. Network Robustness Learning, which is dedicated to learning network robustness with high precision and high speed, provides a powerful tool to analyze network robustness by replacing simulations. In this paper, a novel versatile and unified robustness learning approach via graph transformer (NRL-GT) is proposed, which accomplishes the task of controllability robustness learning and connectivity robustness learning from multiple aspects including robustness curve learning, overall robustness learning, and synthetic network classification. Numerous experiments show that: 1) NRL-GT is a unified learning framework for controllability robustness and connectivity robustness, demonstrating a strong generalization ability to ensure high precision when training and test sets are distributed differently; 2) Compared to the cutting-edge methods, NRL-GT can simultaneously perform network robustness learning from multiple aspects and obtains superior results in less time. NRL-GT is also able to deal with complex networks of different size with low learning error and high efficiency; 3) It is worth mentioning that the backbone of NRL-GT can serve as a transferable feature learning module for complex networks of different size and different downstream tasks.