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TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics by Liu, Chang; Ding, Jingtao; Song, Yiwen; Li, Yong is a scholarly article available to read on EtoBox.

What is TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics about?

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 e

Author

Liu, Chang; Ding, Jingtao; Song, Yiwen; Li, Yong

Published

2024

Language

EN