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. Hyperbolic geometry provides a more suitable representation space. We propose HERMIT (Hyperbolic Edge-aware RTT Modeling via Integrated Topology), a hybrid framework combining a hyperbolic manifold-preserving temporal GNN with a Random Forest regressor for joint link prediction and RTT prediction. Built on HMPTGN, HERMIT introduces RTT-aware edge features and a learnable edge encoder to improve modeling of evolving link states and routing behavior. The resulting hyperbolic node representations are combined with historical RTT statistics for robust latency prediction. We evaluate HERMIT on a large-scale real Internet dataset spanning 2015-2024. HERMIT consistently outperforms a strong Random Forest baseline using only historical RTT statistics, achieving a 6% RMSE improvement while reducing large errors on heavy-tailed samples. It also surpasses prior hyperbolic TGNN models, including HMPTGN and HTGN, in link prediction performance. These results demonstrate that combining hyperbolic temporal graph learning with tree-based regression provides a scalable solution for RTT prediction in real-world Internet topologies.

翻译：预测互联网往返时延（RTT）对于路由优化、服务质量（QoS）保障和流量工程至关重要，但由于存在长期时间依赖性、动态演化的路由特性以及重尾延迟分布，这一任务仍具有挑战性。尽管时间图神经网络（TGNN）能够对不断演化的网络拓扑进行建模，但现有方法大多在欧几里得空间中运行，难以有效捕捉互联网路由图的分层和无标度结构。双曲几何提供了更适宜的表征空间。我们提出HERMIT（基于集成拓扑的双曲边缘感知RTT建模），这是一种混合框架，结合了保留双曲流形的时间GNN与随机森林回归器，用于联合链路预测和RTT预测。基于HMPTGN，HERMIT引入了RTT感知的边缘特征和可学习的边缘编码器，以改进对演化链路状态和路由行为的建模。生成的双曲节点表示与历史RTT统计特征相结合，实现稳健的延迟预测。我们在2015-2024年的大规模真实互联网数据集上评估了HERMIT。与仅使用历史RTT统计的强随机森林基线相比，HERMIT始终表现更优，RMSE提升6%，同时显著降低了重尾样本上的大误差。在链路预测性能上，它也超越了先前的双曲TGNN模型（包括HMPTGN和HTGN）。这些结果表明，将双曲时间图学习与基于树的回归相结合，为真实互联网拓扑中的RTT预测提供了可扩展的解决方案。