GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization

2026-08-24

AI Summary

本文提出GATNextHop模型，利用图注意力网络近似最短路径并跨拓扑泛化，以解决动态或大规模网络中传统算法的可扩展性问题。

Abstract

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.

Problem

• Shortest Path
• Network Topology
• Scalability
• Graph Attention Network
• Generalization

Innovation

• Graph Attention Network
• Cross-Topology Generalization
• Shortest Path Routing