Wavelet-Enhanced Graph Neural Networks: Towards Non-Parametric Network Traffic Modeling

Carlos Güemes Palau, Miquel Ferrior Galmés, Jordi Paillisse Vilanova, Albert López Brescó, Pere Barlet Ros, Albert Cabellos Aparicio

Abstract

Network modeling is crucial for the design, management, and optimization of modern telecommunications and data networks. Recent advancements in Machine Learning (ML), specifically Graph Neural Networks (GNNs), offer promising solutions but rely on parameterized traffic representations, necessitating retraining for new traffic patterns. This paper explores integrating the Discrete Wavelet Transform (DWT) with GNNs to enhance network traffic modeling. By leveraging wavelets, which decompose signals into both time and frequency components, we aim to encode traffic patterns without assuming specific distributions, improving model adaptability and accuracy. We modify the state-of-the-art RouteNet-Fermi model to incorporate wavelet-based traffic encoding and evaluate its performance across different synthetic and real traffic scenarios. Our findings show that wavelet-based encoding handles unseen traffic distributions with minimal impact on performance, unlike traditional parameter-based approaches. This work represents a step forward in bridging the gap between non-parametric traffic representation and advanced network modeling, offering a promising solution for dynamic and complex network environments.

Resources

The paper can be found at ACM Digital Library: [PAPER] The repository with the code can be found here: [CODE]