Networking and Internet Architecture

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

This paper proposes a Graph Neural Network model to predict network flow-level traffic (NetFlow) by representing network traffic as heterogeneous graphs with IP, Port, and Connection nodes. The approach demonstrates strong performance in identifying connection endpoints while maintaining competitive feature reconstruction compared to traditional forecasting baselines.