GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization
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Computer Science > Machine Learning
Title:GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization
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.
| Comments: | Sixth Annual Computer Science Conference for CSU Undergraduates |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.23917 [cs.LG] |
| (or arXiv:2608.23917v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.23917
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Katerina Potika Dr [view email][v1] Mon, 24 Aug 2026 23:50:52 UTC (562 KB)
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