用图卷积+门控循环网络预测复杂网络流量，精度高且稳定。
Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation
- 融合GCN与GRU，同时捕捉节点空间依赖与时间演变
- 在Abilene数据集上多指标优于主流深度学习方法
- 适合需要高精度流量预测的网络运维场景
本研究针对复杂拓扑环境下网络流量预测的挑战，提出一种时空建模方法，将图卷积网络（GCN）与门控循环单元（GRU）相结合。GCN模块捕获网络节点间的空间依赖关系，GRU模块建模流量数据的时间演化过程，实现对未来流量模式的精准预测。在真实世界Abilene网络流量数据集上的全面实验验证了该模型的有效性，其性能在多个指标上优于多种主流深度学习方法。通过一系列消融实验，分析了图卷积层数、不同时间建模策略以及邻接矩阵构建方法对性能的影响。结果表明，该方法在复杂网络流量预测场景中展现出优异的性能、强鲁棒性与良好泛化能力。
原文摘要 · Abstract (English)
This study focuses on the challenge of predicting network traffic within complex topological environments. It introduces a spatiotemporal modeling approach that integrates Graph Convolutional Networks (GCN) with Gated Recurrent Units (GRU). The GCN component captures spatial dependencies among network nodes, while the GRU component models the temporal evolution of traffic data. This combination allows for precise forecasting of future traffic patterns. The effectiveness of the proposed model is validated through comprehensive experiments on the real-world Abilene network traffic dataset. The model is benchmarked against several popular deep learning methods. Furthermore, a set of ablation experiments is conducted to examine the influence of various components on performance, including changes in the number of graph convolution layers, different temporal modeling strategies, and methods for constructing the adjacency matrix. Results indicate that the proposed approach achieves superior performance across multiple metrics, demonstrating robust stability and strong generalization capabilities in complex network traffic forecasting scenarios.
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