mlsanityData to 7 Oct 2026

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

arXiv 1901.08113 ↗First posted 23 Jan 2019cs.NIcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 8; 2023: 5; 2024: 2; 2025: 1; 2026: 1.

27 papers.

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  1. 2021Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunitiescited by 22
  2. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21
  3. 2022Reinforcement learning on graphs: A surveycited by 18
  4. 2022RouteNet-Fermi: Network Modeling with Graph Neural Networkscited by 17
  5. 2022RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluationcited by 15
  6. 2021Unveiling the potential of Graph Neural Networks for robust Intrusion Detectioncited by 14
  7. 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineeringcited by 10
  8. 2021GDDR: GNN-based Data-Driven Routingcited by 9
  9. 2021Is Machine Learning Ready for Traffic Engineering Optimization?cited by 8
  10. 2024Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networkscited by 8
  11. 2021IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systemscited by 6
  12. 2024Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networkscited by 6
15 more
  1. 2021The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networkscited by 5
  2. 2021Scaling Graph-based Deep Learning models to larger networkscited by 5
  3. 2023QDC: Quantum Diffusion Convolution Kernels on Graphscited by 4
  4. 2022Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networkscited by 2
  5. 2022QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theorycited by 2
  6. 2022Graph Neural Modeling of Network Flowscited by 2
  7. 2023Detecting Contextual Network Anomalies with Graph Neural Networkscited by 2
  8. 2021Time-based Dynamic Controllability of Disjunctive Temporal Networks with Uncertainty: A Tree Search Approach with Graph Neural Network Guidancecited by 1
  9. 2022Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networkscited by 1
  10. 2022Analyzing the Traffic of MANETs using Graph Neural Networkscited by 1
  11. 2021Machine Learning Applications in the Routing in Computer Networkscited by 0
  12. 2023Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Predictioncited by 0
  13. 2023Low Complexity Approaches for End-to-End Latency Predictioncited by 0
  14. 2025QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learningcited by 0
  15. 2026From Simulation to Deep Learning: Survey on Network Performance Modeling Approachescited by 0