# Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks

arXiv: https://arxiv.org/abs/2503.24203 · HTML: https://mlsanity.com/paper/2503.24203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem](https://mlsanity.com/paper/1906.01227/) (2019)
- [Combinatorial optimization and reasoning with graph neural networks](https://mlsanity.com/paper/2102.09544/) (2021)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [Generalization and Representational Limits of Graph Neural Networks](https://mlsanity.com/paper/2002.06157/) (2020)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [What Can Neural Networks Reason About?](https://mlsanity.com/paper/1905.13211/) (2019)
- [Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies](https://mlsanity.com/paper/2002.05120/) (2020)
- [On Representing Linear Programs by Graph Neural Networks](https://mlsanity.com/paper/2209.12288/) (2022)
- [Distributed Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2011.09430/) (2020)
- [Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems](https://mlsanity.com/paper/2310.10603/) (2023)
- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation](https://mlsanity.com/paper/2202.13956/) (2022)
- [Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP](https://mlsanity.com/paper/2110.11927/) (2021)
- [Teal: Learning-Accelerated Optimization of WAN Traffic Engineering](https://mlsanity.com/paper/2210.13763/) (2022)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions](https://mlsanity.com/paper/2009.13697/) (2020)
- [FIGRET: Fine-Grained Robustness-Enhanced Traffic Engineering](https://mlsanity.com/paper/2405.04932/) (2024)
- [Gemini: Practical Reconfigurable Datacenter Networks with Topology and Traffic Engineering](https://mlsanity.com/paper/2110.08374/) (2021)
- [GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks](https://mlsanity.com/paper/2110.11552/) (2021)
- [Decouple and Decompose: Scaling Resource Allocation with DeDe](https://mlsanity.com/paper/2412.11447/) (2024)

## Abstract (arXiv)

Traffic Engineering (TE) in large-scale networks like cloud Wide Area Networks (WANs) and Low Earth Orbit (LEO) satellite constellations is a critical challenge. Although learning-based approaches have been proposed to address the scalability of traditional TE algorithms, their practical application is often hindered by a lack of generalization, high training overhead, and a failure to respect link capacities. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale network scenarios, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of "predicting the optimal TE solution" into "predicting the optimal TE algorithm", which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact underlying network topology or traffic patterns, and is able to very efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. We train and evaluate TELGEN with random and real-world topologies, with networks of up to 5000 nodes and 3.6x10^6 links in testing. TELGEN shows less than 3% optimality gap while ensuring feasibility in all testing scenarios, even when the test network has 2-20x more nodes than the largest training network. It also saves up to 84% TE solving time than traditional interior-point method, and reduces up to 79.6% training time per epoch than the state-of-the-art learning-based algorithm.