# Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

arXiv: https://arxiv.org/abs/1901.08113 · HTML: https://mlsanity.com/paper/1901.08113/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 27 of 27)

- [Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities](https://mlsanity.com/paper/2112.14792/) (2021)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [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)
- [Unveiling the potential of Graph Neural Networks for robust Intrusion Detection](https://mlsanity.com/paper/2107.14756/) (2021)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [GDDR: GNN-based Data-Driven Routing](https://mlsanity.com/paper/2104.09919/) (2021)
- [Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks](https://mlsanity.com/paper/2407.20597/) (2024)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems](https://mlsanity.com/paper/2109.06715/) (2021)
- [Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks](https://mlsanity.com/paper/2406.05482/) (2024)
- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [Scaling Graph-based Deep Learning models to larger networks](https://mlsanity.com/paper/2110.01261/) (2021)
- [QDC: Quantum Diffusion Convolution Kernels on Graphs](https://mlsanity.com/paper/2307.11234/) (2023)
- [Graph Neural Modeling of Network Flows](https://mlsanity.com/paper/2209.05208/) (2022)
- [Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks](https://mlsanity.com/paper/2205.08347/) (2022)
- [QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory](https://mlsanity.com/paper/2207.06336/) (2022)
- [Detecting Contextual Network Anomalies with Graph Neural Networks](https://mlsanity.com/paper/2312.06342/) (2023)
- [Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidance](https://mlsanity.com/paper/2108.01068/) (2021)
- [Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks](https://mlsanity.com/paper/2203.15030/) (2022)
- [Analyzing the Traffic of MANETs using Graph Neural Networks](https://mlsanity.com/paper/2212.08923/) (2022)
- [Machine Learning Applications in the Routing in Computer Networks](https://mlsanity.com/paper/2104.01946/) (2021)
- [Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Prediction](https://mlsanity.com/paper/2301.13536/) (2023)
- [Low Complexity Approaches for End-to-End Latency Prediction](https://mlsanity.com/paper/2302.00004/) (2023)
- [QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning](https://mlsanity.com/paper/2512.19696/) (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 critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case R2=0.86) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.