The traffic in today's networks is increasingly influenced by the interactions among network nodes as well as by the temporal fluctuations in the demands of the nodes. Traditional statistical prediction methods are becoming obsolete due to their inability to address the non-linear and dynamic spatio-temporal dependencies present in today's network traffic. The most promising direction of research today is graph neural networks (GNNs) based prediction approaches that are naturally suited to handle graph-structured data. Unfortunately, the state-of-the-art GNN approaches separate the modeling of spatial and temporal information, resulting in the loss of important information about joint dependencies. These GNN based approaches further do not model information at both local and global scales simultaneously, leaving significant room for improvement. To address these challenges, we propose NetSight. NetSight learns joint spatio-temporal dependencies simultaneously at both global and local scales from the time-series of measurements of any given network metric collected at various nodes in a network. Using the learned information, NetSight can then accurately predict the future values of the given network metric at those nodes in the network. We propose several new concepts and techniques in the design of NetSight, such as spatio-temporal adjacency matrix and node normalization. Through extensive evaluations and comparison with prior approaches using data from two large real-world networks, we show that NetSight significantly outperforms all prior state-of-the-art approaches. We will release the source code and data used in the evaluation of NetSight on the acceptance of this paper.
View on arXiv@article{xing2025_2505.07034,
title={ NetSight: Graph Attention Based Traffic Forecasting in Computer Networks },
author={ Jinming Xing and Guoheng Sun and Hui Sun and Linchao Pan and Shakir Mahmood and Xuanhao Luo and Muhammad Shahzad },
journal={arXiv preprint arXiv:2505.07034},
year={ 2025 }
}