mlsanityData to 7 Oct 2026

Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

arXiv 2112.14792 ↗First posted 29 Dec 2021cs.NI cs.LG eess.SPcited by 22

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Abstract (arXiv)

Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, configurations, traffic flows). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real networks. As a result, such models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in its unprecedented generalization capabilities when applied to other networks and configurations unseen during training, which is a critical feature for achieving practical data-driven solutions for networking. This article comprises a brief tutorial on GNNs and their possible applications to communication networks. To showcase the potential of this technology, we present two use cases with state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, we delve into the key open challenges and opportunities yet to be explored in this novel research area.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 29 Dec 2021.

Builds on

11 of 25 references.

References · 11

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  5. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  6. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  7. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 80
  8. 2020Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networkingcited by 70
  9. 2018Experience-driven Networking: A Deep Reinforcement Learning based Approachcited by 41
  10. 2019Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDNcited by 27
  11. 2021IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systemscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 4; 2025: 7; 2026: 3.

22 papers.

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  1. 2023Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothingcited by 67
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2022RouteNet-Fermi: Network Modeling with Graph Neural Networkscited by 17
  4. 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineeringcited by 10
  5. 2025AutoG: Towards automatic graph construction from tabular datacited by 6
  6. 2024Graph Parsing Networkscited by 4
  7. 2024Survey of Graph Neural Network for Internet of Things and NextG Networkscited by 4
  8. 2024Hybrid variable spiking graph neural networks for energy-efficient scientific machine learningcited by 3
  9. 2025Enhancing Resilience for IoE: A Perspective of Networking-Level Safeguardcited by 3
  10. 2023Building a Graph-based Deep Learning network model from captured traffic tracescited by 2
  11. 2023Detecting Contextual Network Anomalies with Graph Neural Networkscited by 2
  12. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
10 more
  1. 2025Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputationcited by 2
  2. 2025Generalization in Reinforcement Learning for Radio Access Networkscited by 2
  3. 2022Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approachcited by 1
  4. 2025Learning Time-Varying Graph Signals via Koopmancited by 1
  5. 2023Data-driven Intra-Autonomous Systems Graph Generatorcited by 0
  6. 2025DGTEN: A Robust Deep Gaussian based Graph Neural Network for Dynamic Trust Evaluation with Uncertainty-Quantification Supportcited by 0
  7. 2025Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learningcited by 0
  8. 2026Parallelizing Node-Level Explainability in Graph Neural Networkscited by 0
  9. 2026Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Studycited by 0
  10. 2026TopoEmbedX: A General Framework for Representation Learning on Topological Domainscited by 0