mlsanityData to 7 Oct 2026

Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

arXiv 2212.04047 ↗First posted 8 Dec 2022cs.IT cs.AI math.ITcited by 8

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Abstract (arXiv)

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications. This article aims to provide a comprehensive overview of the interplay between GNNs and wireless communications, including GNNs for wireless communications (GNN4Com) and wireless communications for GNNs (Com4GNN). In particular, we discuss GNN4Com based on how graphical models are constructed and introduce Com4GNN with corresponding incentives. We also highlight potential research directions to promote future research endeavors for GNNs in wireless communications.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 8 Dec 2022.

Builds on

11 of 15 references.

References · 11

  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  2. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  3. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  4. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 80
  5. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  6. 2019Graph Embedding based Wireless Link Scheduling with Few Training Samplescited by 30
  7. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21
  8. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 19
  9. 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
  10. 2020A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctionscited by 6
  11. 2021Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 2.

8 papers.

  1. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  2. 2025Residual connections provably mitigate oversmoothing in graph neural networkscited by 5
  3. 2023Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?cited by 2
  4. 2024Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpointcited by 0
  5. 2024Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networkscited by 0
  6. 2025Graph Attention Network for Optimal User Association in Wireless Networkscited by 0
  7. 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networkscited by 0
  8. 2026AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networkscited by 0