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This research applies Graph Neural Networks to model network topology as evolving graphs of IP, Port, and Connection nodes for traffic forecasting. While the application targets NetFlow prediction, the core innovation lies in leveraging GNN architectures to capture structural dependencies within network data, placing machine learning at the center of the technical contribution.
Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
arXiv:2604.20483v3 Announce Type: replace-cross Abstract: In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structure and the connection features. We use sliding-windows to split the network traffic in equal-sized heterogeneous bidirectional graphs containing IP, Port, and Connection nodes. We then use the GNN to model the evolution of the graph structure and the connection features. Our approach shows superior results when identifying the Port and IP to which connections attach, while feature reconstruction remains competitive with strong forecasting baselines. Overall, our work showcases the use of GNNs for per-flow NetFlow prediction.
Abstract:In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurately modelling the graph structure and the connection features. We use sliding-windows to split the network traffic in equal-sized heterogeneous bidirectional graphs containing IP, Port, and Connection nodes. We then use the GNN to model the evolution of the graph structure and the connection features. Our approach shows superior results when identifying the Port and IP to which connections attach, while feature reconstruction remains competitive with strong forecasting baselines. Overall, our work showcases the use of GNNs for per-flow NetFlow prediction.
Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
arXiv:2604.20483 [cs.NI]
(or arXiv:2604.20483v3 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2604.20483 Focus to learn more arXiv-issued DOI via DataCite
Proc. IEEE MNC, 2026, pp. 1-6
https://doi.org/10.1109/MNC69143.2026.11680744 Focus to learn more DOI(s) linking to related resources
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