Toward Robust End-to-End Delay Prediction: A GNN Approach With Routing-Aware Attention and Masked Subgraph Sampling

The end-to-end delay prediction is critical for intelligent network management, particularly in latency-sensitive and dynamic environments. While recent deep learning (DL) models have shown promising results, their reliance on sequential encoding of routing paths limits generalization to unseen routing schemes. In this article, we propose a robust graph neural network (GNN)-based delay prediction model that overcomes this limitation by introducing a global routing representation and a routing-aw