用图注意力网络学习跨拓扑最短路径，提升路由效率
GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization
- 用GAT模型学习最短路径决策规则
- 在真实ISP网络上准确率超90%，推理速度提升10倍
- 适合需要快速动态路由的大型网络场景
传统最短路径算法（如OSPF使用的Dijkstra）虽能提供精确解，但需为每种网络拓扑重新计算，限制了在动态或大规模网络中的可扩展性。本文提出GATNextHop模型，探究图神经网络（图注意力网络）是否能近似最短路径并实现跨拓扑泛化。通过在合成图上训练，并在来自Internet Topology Zoo的真实ISP网络上评估，旨在基准测试模型学习路由启发式策略在不同网络结构间的迁移能力。性能评估涵盖准确率、推理速度与泛化能力，对比GNN与Dijkstra算法，量化学习方法与经典方法之间的权衡。
原文摘要 · Abstract (English)
Common shortest-path algorithms, such as Dijkstra's (SPF), that OSPF uses, provide exact routing solutions but must be recomputed for each network topology, limiting scalability in dynamic or large-scale networks. This paper proposes the GATNextHop model to determine whether a Graph Neural Network, namely the Graph Attention Network, can approximate shortest paths and generalize across topologies. By training on synthetic graphs and evaluating on real-world Internet Service Provider networks from the Internet Topology Zoo, we aim to benchmark our model's ability to learn routing heuristics that transfer across network structures. Performance will be evaluated in terms of accuracy, inference speed, and generalization, comparing the GNN against Dijkstra's algorithm to quantify trade-offs between learned and classical routing approaches.
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