TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics

TL;DR

TDNetGen is a novel framework that uses generative data augmentation to improve the prediction of network resilience, especially in low-data scenarios, by leveraging the joint distribution of network topology and dynamics.

Contribution

It introduces a new resilience prediction framework that enhances accuracy and robustness through generative augmentation of network data, addressing data scarcity issues.

Findings

Achieves prediction accuracy of 85%-95%.

Demonstrates strong augmentation capabilities in low-data regimes.

Proves robustness across three network datasets.

Abstract

Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled network data, facilitating the learning process of the resilience predictor by illuminating the relationship between network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%. Furthermore, the framework still demonstrates a pronounced augmentation capability in extreme low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience. We have open-sourced our code in the following link, https://github.com/tsinghua-fib-lab/TDNetGen.