mlsanityData to 14 Oct 2026

Survey of Graph Neural Network for Internet of Things and NextG Networks

arXiv 2405.17309 ↗First posted 27 May 2024cs.LG cs.NIcited by 4

Not on the map. Why?

Abstract (arXiv)

The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficiently use the resources available and meet the users' requirements. Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights which inherently exhibit complex network structures due to its high performance and accuracy, scalability, adaptability, and resource efficiency. There is a lack of a comprehensive survey that focuses on the applications and advances GNN has made in the context of IoT and Next Generation (NextG) networks. To bridge that gap, this survey starts by providing a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Then we provide a comprehensive survey of the advancements in applying GNNs for IoT from the perspective of data fusion and intrusion detection. Thereafter, we survey the impact GNN has made in improving spectrum awareness. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems. Through this survey, we aim to provide a comprehensive resource for researchers to learn more about GNN in the context of wireless networks, and understand its state-of-the-art use cases while contrasting to other machine learning approaches. Finally, we also discussed the challenges and wide range of future research directions to further motivate the use of GNN for IoT and NextG Networks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 128 of its 409 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

128 of 409 references.

References · 128

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  8. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  9. 2016OpenAI Gymcited by 1,736
  10. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  11. 2016Variational Graph Auto-Encoderscited by 1,382
  12. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
38 more
  1. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  2. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  3. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  4. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  5. 2019Self-Attention Graph Poolingcited by 421
  6. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  7. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  8. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  9. 2018Graph Convolutional Reinforcement Learningcited by 152
  10. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
  11. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  12. 2017Over the Air Deep Learning Based Radio Signal Classificationcited by 121
  13. 2020A Survey on The Expressive Power of Graph Neural Networkscited by 111
  14. 2016Convolutional Radio Modulation Recognition Networkscited by 106
  15. 2018Towards Sparse Hierarchical Graph Classifierscited by 100
  16. 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 99
  17. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 91
  18. 2020Computing Graph Neural Networks: A Survey from Algorithms to Acceleratorscited by 81
  19. 2020Graph Neural Networks: Architectures, Stability and Transferabilitycited by 80
  20. 2019Auto-GNN: Neural Architecture Search of Graph Neural Networkscited by 74
  21. 2019Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolutioncited by 67
  22. 2018Automated Machine Learning: From Principles to Practicescited by 62
  23. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  24. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  25. 2021E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTcited by 56
  26. 2019Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use casecited by 53
  27. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  28. 2020Graph Neural Networks: Taxonomy, Advances and Trendscited by 48
  29. 2019RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDNcited by 47
  30. 2020NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systemscited by 45
  31. 2020A Survey of Adversarial Learning on Graphscited by 42
  32. 2019GraphNAS: Graph Neural Architecture Search with Reinforcement Learningcited by 41
  33. 2021Towards a Standard Feature Set for Network Intrusion Detection System Datasetscited by 40
  34. 2021The Road Towards 6G: A Comprehensive Surveycited by 40
  35. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  36. 20196G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directionscited by 34
  37. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 30
  38. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 28

50 most cited of 128 All 128 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  2. 2025O-RAN Xapps Conflict Prediction Using Graph Convolutional Networkscited by 5
  3. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  4. 2025Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networkscited by 1