# RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation

arXiv: https://arxiv.org/abs/2202.13956 · HTML: https://mlsanity.com/paper/2202.13956/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Experience-driven Networking: A Deep Reinforcement Learning based Approach](https://mlsanity.com/paper/1801.05757/) (2018)
- [Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1901.08113/) (2019)
- [Machine Learning for Networking: Workflow, Advances and Opportunities](https://mlsanity.com/paper/1709.08339/) (2017)
- [Understanding the Modeling of Computer Network Delays using Neural Networks](https://mlsanity.com/paper/1807.08652/) (2018)

## Built on by (most cited first, 15 of 15)

- [RouteNet-Fermi: Network Modeling with Graph Neural Networks](https://mlsanity.com/paper/2212.12070/) (2022)
- [Heterogeneous Sheaf Neural Networks](https://mlsanity.com/paper/2409.08036/) (2024)
- [Learnable Digital Twin for Efficient Wireless Network Evaluation](https://mlsanity.com/paper/2306.06574/) (2023)
- [RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning](https://mlsanity.com/paper/2501.08848/) (2025)
- [Building a Graph-based Deep Learning network model from captured traffic traces](https://mlsanity.com/paper/2310.11889/) (2023)
- [Ordered Topological Deep Learning: a Network Modeling Case Study](https://mlsanity.com/paper/2503.16746/) (2025)
- [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)
- [Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations](https://mlsanity.com/paper/2604.07264/) (2026)
- [ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics](https://mlsanity.com/paper/2206.08258/) (2022)
- [Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks](https://mlsanity.com/paper/2503.24203/) (2025)
- [Optimizing Quantum Key Distribution Network Performance using Graph Neural Networks](https://mlsanity.com/paper/2511.16468/) (2025)
- [M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks](https://mlsanity.com/paper/2512.09797/) (2025)
- [From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches](https://mlsanity.com/paper/2603.28394/) (2026)

## Abstract (arXiv)

Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.