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This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
| Date | Stars |
|---|---|
| 2026-07-31 | 602 |
| 2026-08-02 | 602 |
| 2026-08-06 | 602 |
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# GNN-Communication-Networks This is the repository for the collection of Graph-based Deep Learning for Communication Networks. If you find this repository helpful, you may consider cite our relevant work: * Jianping W, Guangqiu Q, Chunming W, et al. <b>Federated learning for network attack detection using attention-based graph neural networks[J]</b>. Scientific Reports, 2024, 14(1): 19088. [Link](https://www.nature.com/articles/s41598-024-70032-2) * Jiang W. <b>Graph-based Deep Learning for Communication Networks: A Survey[J]</b>. Computer Communications, 2022, 185:40-54. [Link](https://www.sciencedirect.com/science/article/abs/pii/S0140366421004874) * For the surveyed studies in different scenarios, you may check [survey.md](https://github.com/jwwthu/GNN-Communication-Networks/blob/main/survey.md) * Jiang W, Han H, Zhang Y, et al. <b>Graph Neural Networks for Routing Optimization: Challenges and Opportunities[J]</b>. Sustainability, 2024, 16(21): 9239. [Link](https://www.mdpi.com/2071-1050/16/21/9239) Advertisement: 欢迎大家关注我的微信公众号或知乎账号，都叫“网络与通信”，会定期推送网络与通信领域会议截止日期汇总、开源代码论文汇总等推文。 # Other Surveys * He S, Xiong S, Ou Y, et al. <b>An overview on the application of graph neural networks in wireless networks[J]</b>. IEEE Open Journal of the Communications Society, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9618652/) * Suárez-Varela J, Almasan P, Ferriol-Galmés M, et al. <b>Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities[J]</b>. IEEE Network, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9846958/) * Tam P, Song I, Kang S, et al. <b>Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications[J]</b>. Electronics, 2022, 11(20): 3371. [Link](https://www.mdpi.com/1893620) * Ivanov A, Tonchev K, Poulkov V, et al. <b>Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications[J]</b>. Sensors, 2022, 22(15): 5778. [Link](https://www.mdpi.com/1424-8220/22/15/5778) * Lee M, Yu G, Dai H, et al. <b>Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions[J]</b>. IEEE Wireless Communications, 2022, 29(5): 12-19. [Link](https://ieeexplore.ieee.org/abstract/document/9979700/) * Li Y, Xie S, Wan Z, et al. <b>Graph-powered learning methods in the Internet of Things: A survey[J]</b>. Machine Learning with Applications, 2023, 11: 100441. [Link](https://www.sciencedirect.com/science/article/pii/S2666827022001165) * Dong G, Tang M, Wang Z, et al. <b>Graph neural networks in IoT: A survey[J]</b>. ACM Transactions on Sensor Networks, 2023, 19(2): 1-50. [Link](https://dl.acm.org/doi/abs/10.1145/3565973) [GNN4IoT Repository](https://github.com/GuiminDong/GNN4IoT) # Competition * Suárez-Varela J, Ferriol-Galmés M, López A, et al. <b>The graph neural networking challenge: a worldwide competition for education in AI/ML for networks[J]</b>. ACM SIGCOMM Computer Communication Review, 2021, 51(3): 9-16. [Link](https://dl.acm.org/doi/abs/10.1145/3477482.3477485) * Ferriol-Galmés M, Suárez-Varela J, Rusek K, et al. <b>Scaling Graph-based Deep Learning models to larger networks[J]</b>. arXiv preprint arXiv:2110.01261, 2021. [Link](https://arxiv.org/abs/2110.01261) # Tool * Pujol-Perich D, Suárez-Varela J, Ferriol-Galmés M, et al. <b>IGNNITION: fast prototyping of graph neural networks for communication networks[M]</b>//Proceedings of the SIGCOMM'21 Poster and Demo Sessions. 2021: 71-73. [Link](https://dl.acm.org/doi/abs/10.1145/3472716.3472853) * Pujol-Perich D, Suárez-Varela J, Ferriol M, et al. <b>IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems[J]</b>. IEEE Network, 2021. [Link](https://arxiv.org/abs/2109.06715v1) [Code](https://ignnition.org/doc/) # Literature The list would be updated monthly. ## 2026 ### Journal * Hao F, Xiao L, Chongtao G, et al. <b>AoI-driven queue management and power control in V2V networks: A GNN-enhanced MARL a
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Tianfu Wang · USTC · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:95b1d9ffd96aeb75, llm:Repository description and README: 'collection of Graph-based Deep Learning for Communication Networks', topics include graph, graph-convolutional-networks, graph-neural-network, commmunication-networks; contains surveys and papers applying GNNs to networking (routing, federated learning for network attack detection).