mlsanityData to 7 Oct 2026

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

arXiv 2202.13956 ↗First posted 28 Feb 2022cs.NI cs.LGcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 10 of its 49 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

10 of 49 references.

References · 10

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  4. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  5. 2017One pixel attack for fooling deep neural networkscited by 579
  6. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  7. 2018Experience-driven Networking: A Deep Reinforcement Learning based Approachcited by 41
  8. 2019Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDNcited by 27
  9. 2017Machine Learning for Networking: Workflow, Advances and Opportunitiescited by 20
  10. 2018Understanding the Modeling of Computer Network Delays using Neural Networkscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 1; 2025: 6; 2026: 2.

15 papers.

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  1. 2022RouteNet-Fermi: Network Modeling with Graph Neural Networkscited by 17
  2. 2024Heterogeneous Sheaf Neural Networkscited by 10
  3. 2023Learnable Digital Twin for Efficient Wireless Network Evaluationcited by 3
  4. 2025RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learningcited by 3
  5. 2023Building a Graph-based Deep Learning network model from captured traffic tracescited by 2
  6. 2025Ordered Topological Deep Learning: a Network Modeling Case Studycited by 2
  7. 2022Open World Learning Graph Convolution for Latency Estimation in Routing Networkscited by 1
  8. 2023FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Designcited by 1
  9. 2025m4: A Learned Flow-level Network Simulatorcited by 1
  10. 2022ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metricscited by 0
  11. 2025Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networkscited by 0
  12. 2025Optimizing Quantum Key Distribution Network Performance using Graph Neural Networkscited by 0
3 more
  1. 2025M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networkscited by 0
  2. 2026From Simulation to Deep Learning: Survey on Network Performance Modeling Approachescited by 0
  3. 2026Validated Intent Compilation for Constrained Routing in LEO Mega-Constellationscited by 0