# RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN

arXiv: https://arxiv.org/abs/1910.01508 · HTML: https://mlsanity.com/paper/1910.01508/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 30 of 47)

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- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [Learning to Configure Computer Networks with Neural Algorithmic Reasoning](https://mlsanity.com/paper/2211.01980/) (2022)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Pairing Conceptual Modeling with Machine Learning](https://mlsanity.com/paper/2106.14251/) (2021)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [Learnable Digital Twin for Efficient Wireless Network Evaluation](https://mlsanity.com/paper/2306.06574/) (2023)
- [Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflow](https://mlsanity.com/paper/2403.14460/) (2024)
- [QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory](https://mlsanity.com/paper/2207.06336/) (2022)
- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Building a Graph-based Deep Learning network model from captured traffic traces](https://mlsanity.com/paper/2310.11889/) (2023)
- [Metarobotics for Industry and Society: Vision, Technologies, and Opportunities](https://mlsanity.com/paper/2404.00797/) (2024)
- [Biased Backpressure Routing Using Link Features and Graph Neural Networks](https://mlsanity.com/paper/2407.09753/) (2024)
- [NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies](https://mlsanity.com/paper/2410.08238/) (2024)
- [Ordered Topological Deep Learning: a Network Modeling Case Study](https://mlsanity.com/paper/2503.16746/) (2025)
- [RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks](https://mlsanity.com/paper/2511.22321/) (2025)
- [Let the Barbarians In: How AI Can Accelerate Systems Performance Research](https://mlsanity.com/paper/2512.14806/) (2025)
- [Machine Learning for Massive Industrial Internet of Things](https://mlsanity.com/paper/2103.08308/) (2021)
- [Open World Learning Graph Convolution for Latency Estimation in Routing Networks](https://mlsanity.com/paper/2207.14643/) (2022)
- [FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design](https://mlsanity.com/paper/2305.19153/) (2023)
- [m4: A Learned Flow-level Network Simulator](https://mlsanity.com/paper/2503.01770/) (2025)
- [SatQNet: Satellite-assisted Quantum Network Entanglement Routing Using Directed Line Graph Neural Networks](https://mlsanity.com/paper/2604.09306/) (2026)

## Abstract (arXiv)

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.