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This is a Quantitative Study study.

October 16, 2025

Lightweight Graph Networks for AI‐Integrated Network Traffic Prediction: Towards Efficient Edge Computing Solutions

Key Points

The proposed model achieves over 93% prediction accuracy, significantly improving network management processes.
With a reduction of at least 7.9% in mean absolute error, this model outperforms existing traffic prediction methods.
The lightweight graph neural network leverages adaptive graph sampling to streamline predictive analytics.
Improved inference latency of over 10% positions this approach as a viable solution for real-time applications.

Abstract

ABSTRACT Network traffic prediction stands as a cornerstone for the realization of intelligent network management. Traditional cloud‐centric solutions encounter significant challenges, including latency and bandwidth limitations, when tasked with handling real‐time predictive analytics. This paper introduces an innovative approach centered around an edge computing‐based lightweight Graph Neural Network (GNN) model designed to facilitate AI‐integrated traffic forecasting. The proposed methodology achieves model lightness through the employment of adaptive graph sampling techniques, while traffic predictions are executed by seamlessly integrating the formulated GNN model. Our comprehensive experimentation demonstrates that the proposed framework significantly outperforms existing state‐of‐the‐art methodologies in multiple performance metrics. Specifically, our approach achieves a reduction in the mean absolute error by at least 7.9%, along with the lowest root mean squared error. Furthermore, the inference latency is reduced by approximately 10% or more, whereas the prediction accuracy reaches more than 93%, exceeding that of competing methods.

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