发表机构
San José State University（圣何塞州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出GATNextHop模型，探究GAT能否近似最短路径并跨拓扑泛化，通过合成图训练、真实ISP网络评估，对比Dijkstra算法分析二者权衡以评估模型性能。
AI 中文摘要
OSPF所使用的Dijkstra算法（最短路径优先SPF）等常见最短路径算法能提供精确的路由解决方案，但需针对每个网络拓扑重新计算，限制了其在动态或大规模网络中的可扩展性。本文提出GATNextHop模型，旨在探究图注意力网络（Graph Attention Network，GAT）能否近似最短路径并实现跨拓扑泛化。通过在合成图上训练、在来自Internet Topology Zoo的真实互联网服务提供商网络上评估，我们将从准确率、推理速度和泛化性方面评估模型性能，将该图神经网络（GNN）与Dijkstra算法对比，以量化学习型路由方法与经典路由方法之间的权衡。
英文摘要
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.
CommentsSixth Annual Computer Science Conference for CSU Undergraduates
