AI 中文总结
本研究针对网络数字孪生（NDTs）的支撑技术需求，提出混合时空图神经网络（HSTGNN）架构，结合三种消息传递范式，在多项指标上显著优于基准方法，可支撑下一代智能基础设施孪生。
AI 中文摘要
网络数字孪生（Network Digital Twins, NDTs）通过在对实时基础设施施加控制动作前预测系统行为，实现主动网络管理与优化，支持互联网服务提供商（ISP）网络和广域网（WAN）的关键操作。然而，要发挥NDTs承诺的优异性能，需关键支撑技术。由于移动网络可建模为图，图神经网络（GNN）等基于图的架构在网络行为建模中已展现出良好性能。本研究提出一种新型混合时空图神经网络（Hybrid Spatial-Temporal Graph Neural Network, HSTGNN）架构，不同于单分支GNN方法，采用多尺度设计，结合三种互补的消息传递范式：局部邻域聚合、谱滤波以及可学习的基于注意力的加权。与其他方法对比，所提HSTGNN表现出更优性能，决定系数约为0.8816，比最优基准方法ChebNet高17.5%；此外，HSTGNN还取得了最低的平均绝对误差（MAE）0.0300和均方根误差（RMSE）0.0458，显著优于基准框架，验证了该框架支撑NDTs的能力。
英文摘要
Network Digital Twins (NDTs) enable proactive network management and optimization by predicting system behavior before control actions are applied to live infrastructures, supporting critical operations in Internet Service Provider (ISP) networks and wide-area networks (WANs). However, to anchor the superior performance NDTs promise to provide, key enabler techniques are required. Given that mobile networks are modeled as graphs, graph-based architectures such as graph neural networks (GNNs) have shown promising performance in modeling network behavior. This work proposes a novel Hybrid Spatial-Temporal Graph Neural Network (HSTGNN) architecture. Unlike single-branch GNN approaches, we propose a multi-scale design that combines three complementary message-passing paradigms: local neighborhood aggregation, spectral filtering, and learnable attention-based weighting. When benchmarked against other approaches, the proposed HSTGNN achieved superior performance delivering a coefficient of determination score of approximately 0.8816, 17.5\% better than the best baseline ChebNet. Furthermore, HSTGNN achieved the lowest Mean Absolute Error (MAE) of 0.0300, and Root Mean Squared Error (RMSE) of 0.0458, significantly outperforming baseline frameworks and certifying the proposed framework's capability in enabling NDTs.
CommentsAccepted at IEEE GLOBECOM 2026
