# RouteNet-Fermi: Network Modeling with Graph Neural Networks

arXiv: https://arxiv.org/abs/2212.12070 · HTML: https://mlsanity.com/paper/2212.12070/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [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)
- [From Local Structures to Size Generalization in Graph Neural Networks](https://mlsanity.com/paper/2010.08853/) (2020)
- [Graphon Neural Networks and the Transferability of Graph Neural Networks](https://mlsanity.com/paper/2006.03548/) (2020)
- [Subgroup Generalization and Fairness of Graph Neural Networks](https://mlsanity.com/paper/2106.15535/) (2021)
- [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)
- [Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities](https://mlsanity.com/paper/2112.14792/) (2021)
- [Machine Learning for Networking: Workflow, Advances and Opportunities](https://mlsanity.com/paper/1709.08339/) (2017)
- [RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation](https://mlsanity.com/paper/2202.13956/) (2022)
- [Understanding the Modeling of Computer Network Delays using Neural Networks](https://mlsanity.com/paper/1807.08652/) (2018)
- [Digital Twin Network: Opportunities and Challenges](https://mlsanity.com/paper/2201.01144/) (2022)

## Built on by (most cited first, 17 of 17)

- [Heterogeneous Sheaf Neural Networks](https://mlsanity.com/paper/2409.08036/) (2024)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (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)
- [NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies](https://mlsanity.com/paper/2410.08238/) (2024)
- [Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics](https://mlsanity.com/paper/2410.10377/) (2024)
- [Improving the Real-Data Driven Network Evaluation Model for Digital Twin Networks](https://mlsanity.com/paper/2405.08473/) (2024)
- [Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network](https://mlsanity.com/paper/2406.06595/) (2024)
- [Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin](https://mlsanity.com/paper/2502.11505/) (2025)
- [Energy-Efficient Dynamic Training and Inference for GNN-Based Network Modeling](https://mlsanity.com/paper/2503.18706/) (2025)
- [Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks](https://mlsanity.com/paper/2503.24203/) (2025)
- [Bridging the Gap Between Simulated and Real Network Data Using Transfer Learning](https://mlsanity.com/paper/2510.00956/) (2025)
- [M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks](https://mlsanity.com/paper/2512.09797/) (2025)
- [From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction](https://mlsanity.com/paper/2512.20885/) (2025)
- [From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches](https://mlsanity.com/paper/2603.28394/) (2026)
- [GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization](https://mlsanity.com/paper/2608.23917/) (2026)

## Abstract (arXiv)

Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e.g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network.