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

arXiv: https://arxiv.org/abs/2212.04047 · HTML: https://mlsanity.com/paper/2212.04047/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions](https://mlsanity.com/paper/2009.13697/) (2020)
- [Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks](https://mlsanity.com/paper/2107.07198/) (2021)

## Built on by (most cited first, 8 of 8)

- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Residual connections provably mitigate oversmoothing in graph neural networks](https://mlsanity.com/paper/2501.00762/) (2025)
- [Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?](https://mlsanity.com/paper/2307.12480/) (2023)
- [Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint](https://mlsanity.com/paper/2405.05802/) (2024)
- [Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks](https://mlsanity.com/paper/2410.17118/) (2024)
- [Graph Attention Network for Optimal User Association in Wireless Networks](https://mlsanity.com/paper/2505.16347/) (2025)
- [Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks](https://mlsanity.com/paper/2603.16881/) (2026)
- [AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks](https://mlsanity.com/paper/2609.20271/) (2026)

## 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.