AI RESEARCH
Temporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction
arXiv CS.LG
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ArXi:2605.28155v1 Announce Type: new Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet remains challenging due to long-term temporal dependencies, evolving routing dynamics, and heavy-tailed latency distributions. While Temporal Graph Neural Networks (TGNNs) can model evolving network topologies, most existing approaches operate in Euclidean space, which poorly captures the hierarchical and scale-free structure of Internet routing graphs.