https://github.com/jwwthu/GNN-Communication-Networks

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
https://github.com/jwwthu/GNN-Communication-Networks
commmunication-networks graph graph-convolutional-networks graph-neural-network
Last synced: 18 days ago
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This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
Host: GitHub
URL: https://github.com/jwwthu/GNN-Communication-Networks
Owner: jwwthu
Created: 2021-03-31T08:23:23.000Z (about 5 years ago)
Default Branch: main
Last Pushed: 2026-01-31T08:02:10.000Z (5 months ago)
Last Synced: 2026-02-14T16:29:23.464Z (4 months ago)
Topics: commmunication-networks, graph, graph-convolutional-networks, graph-neural-network
Homepage:
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Stars: 573
Watchers: 19
Forks: 86
Open Issues: 1
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-telco - GNN-Communication-Networks - 04]` - Curated collection of GNN research for communication networks. Covers traffic prediction, routing, spectrum sensing. (Related Lists / Notable GitHub Issues & Discussions)
README

          # 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. Federated learning for network attack detection using attention-based graph neural networks[J]. Scientific Reports, 2024, 14(1): 19088. [Link](https://www.nature.com/articles/s41598-024-70032-2)

* Jiang W. Graph-based Deep Learning for Communication Networks: A Survey[J]. 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. Graph Neural Networks for Routing Optimization: Challenges and Opportunities[J]. Sustainability, 2024, 16(21): 9239. [Link](https://www.mdpi.com/2071-1050/16/21/9239)

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# Other Surveys

* He S, Xiong S, Ou Y, et al. An overview on the application of graph neural networks in wireless networks[J]. 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. Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities[J]. IEEE Network, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9846958/)

* Tam P, Song I, Kang S, et al. Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications[J]. Electronics, 2022, 11(20): 3371. [Link](https://www.mdpi.com/1893620)

* Ivanov A, Tonchev K, Poulkov V, et al. Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications[J]. Sensors, 2022, 22(15): 5778. [Link](https://www.mdpi.com/1424-8220/22/15/5778)

* Lee M, Yu G, Dai H, et al. Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions[J]. IEEE Wireless Communications, 2022, 29(5): 12-19. [Link](https://ieeexplore.ieee.org/abstract/document/9979700/)

* Li Y, Xie S, Wan Z, et al. Graph-powered learning methods in the Internet of Things: A survey[J]. Machine Learning with Applications, 2023, 11: 100441. [Link](https://www.sciencedirect.com/science/article/pii/S2666827022001165)

* Dong G, Tang M, Wang Z, et al. Graph neural networks in IoT: A survey[J]. 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. The graph neural networking challenge: a worldwide competition for education in AI/ML for networks[J]. 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. Scaling Graph-based Deep Learning models to larger networks[J]. 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. IGNNITION: fast prototyping of graph neural networks for communication networks[M]//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. IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems[J]. IEEE Network, 2021. [Link](https://arxiv.org/abs/2109.06715v1) [Code](https://ignnition.org/doc/)

# Literature

The list would be updated monthly.

## 2026

### Journal

* Li Y, Feng L, Zhu Z, et al. EM-GAT: An Edge-enhanced Multi-hop Graph Attention Network for Network Intrusion Detection[J]. Ad Hoc Networks, 2026: 104292. [Link](https://www.sciencedirect.com/science/article/pii/S1570870526001587)

* Mittal A, Singh S K. Mobility-Aware MIMO Channel Estimation for RIS-Assisted Terahertz Systems via Meta-Learned Spatio-Temporal Graph Neural Networks[J]. Physical Communication, 2026: 103159. [Link](https://www.sciencedirect.com/science/article/pii/S1874490726001680)

* Yang P, Zheng T, Zhang S, et al. Graph-Meta-Reinforcement-Learning-Based Algorithm for Task Offloading in Vehicular Edge-Cloud Collaboration[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11219184/)

* Yuan Y, Shen X, Sun L, et al. Key Nodes Prediction and Cascading Failures Mitigation in Dynamic Traffic UASNs Via a GCN-LSTM Model[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11319241/)

* Wang K, Jiang Q, Wu Y, et al. STATGRAPH: Effective in-vehicle intrusion detection via multi-view statistical graph learning[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11267138/)

* Teng M, Li X, Zhang X, et al. Graph-Based Spatiotemporal RL Framework for Sequential Task Offloading in Multi-UAV Systems[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11264345/)

* Wang X, Wang Z, Cheng N, et al. Graph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11260947/)

* Li Y, Xu X, Xu J. AI-Enhanced Hierarchical Routing with Q-learning and Graph Neural Networks for 6G-Enabled Internet of Vehicles[J]. Computer Networks, 2026: 112206. [Link](https://www.sciencedirect.com/science/article/pii/S1389128626002185)

* Sharma T, Shieh C S, Forng M H, et al. Enhancing Internet of Things Security: A Federated Learning‐Based Hybrid Model With Graph Neural Networks and Transformers[J]. International Journal of Communication Systems, 2026, 39(6): e70446. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70446)

* Rajkumar K, Sivakumar V, Shunmugapriya B, et al. Graph Learning–Based Spatial–Temporal Graph Convolutional Neural Network for Overlap Detection and Optimal Link‐State Routing for Effective Data Transmission in Visual Sensor Network[J]. International Journal of Communication Systems, 2026, 39(5): e70439. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70439)

* Zhan C, Liu W, Song K, et al. Joint UAV placement and dependent task offloading in multi-UAV MEC networks: A graph attention enhanced DRL approach[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11224636/)

* Yang H, Pan L, Liu S. Learning reward functions via GNNs for multi-agent task placement in edge-cloud LLM services[J]. Journal of Network and Computer Applications, 2026: 104457. [Link](https://www.sciencedirect.com/science/article/pii/S1084804526000329)

* Ramesh B, Guganathan L, Arunachalam K P, et al. Non‐Convolutional Decision Transformer Graph Neural Network for Trust‐Aware Routing in 6G‐Enabled MANET‐IoT Networks[J]. International Journal of Communication Systems, 2026, 39(6): e70437. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70437)

* Ampratwum I, Elhadef M, Nayak A. Predictive Maintenance and Reliability in Intelligent 5G Networks based on Graph Neural Networks[J]. IEEE Access, 2026. [Link](https://ieeexplore.ieee.org/abstract/document/11433666/)

* Abbass W, Abbas N, Majeed U. Prevail: A 6G spatio-temporal graph learning framework for traffic vulnerability prediction[J]. Computer Communications, 2026: 108466. [Link](https://www.sciencedirect.com/science/article/pii/S0140366426000563)

* Kagi S, Chandra K R, Sreethar S, et al. Joint Power and Delay Optimization Based Resource Allocation in Mu‐MIMO‐OFDM System Using Optimized Enhanced Elman Spiking Sparse Graph Neural Network[J]. Transactions on Emerging Telecommunications Technologies, 2026, 37(2): e70337. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.70337)

* Xu J, Zhang R, Lin D, et al. Sparsity-Resilient QoS Prediction via ε-DP Enhanced Subgraph-Inductive GNNs in Internet of Services[J]. Computer Networks, 2026: 112085. [Link](https://www.sciencedirect.com/science/article/pii/S1389128626000976)

* Wu Q, Liu Q, He Y, et al. UGV-Assisted Task Allocation for UAVs: A Heterogeneous Graph Reinforcement Learning Approach[J]. IEEE Transactions on Services Computing, 2026. [Link](https://ieeexplore.ieee.org/abstract/document/11342382/)

* Wang Z, Yuan F, Qiu H, et al. Large-Scale BGP Routing Anomaly Detection Based on Graph Attention Auto-Encoder[J]. IEEE Transactions on Network and Service Management, 2026, 23: 487-501. [Link](https://ieeexplore.ieee.org/abstract/document/11328038/)

* Wang C, Dong M, Yuan Y, et al. Energy-efficient trajectory planning for UAV-assisted communication recovery using multi-agent graph reinforcement learning[J]. Ad Hoc Networks, 2026, 184: 104145. [Link](https://www.sciencedirect.com/science/article/pii/S1570870526000119)

* Xu J, Chen C, Dai Q, et al. Sparse QoS prediction for cloud services via inductive subgraph pattern aware graph neural network[J]. Computer Communications, 2026, 248: 108415. [Link](https://www.sciencedirect.com/science/article/pii/S0140366426000058)

### Conference

* Afrin F, Moghim N, Bouk S H, et al. Multi-scale Graph Neural Network for Low-SNR Wireless Signal Classification[C]//2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC). IEEE, 2026: 1-7. [Link](https://ieeexplore.ieee.org/abstract/document/11366565/)

## 2025

### Journal

* Du Y, Xu S, Chen G, et al. A GNN-based Distributed Beamforming Design for MIMO Cell-Free ISAC Networks[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11143948/)

* Li Y, Li J, Yu C, et al. A hierarchical conflict resolution framework with graph transformer-based reinforcement learning for heterogeneous UAV networks[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11146533/)

* Ma X, Hu J, Liang S, et al. Federated learning and resource-aware graph neural network for intrusion detection in 6G-IoT driven healthcare system[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10977998/)

* Singh P, Hazarika B, Huang W J. Graph-based Federated Multi-Agent DRL for Semantic and Intent-Aware V2X Communication[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11186815/)

* Feys T, Van der Perre L, Rottenberg F. Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach[J]. IEEE Journal of Selected Topics in Signal Processing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11103722/)

* Yang J, Li B, Zhang X, et al. A Graph Attention Mechanism-Based Scheme for User Access and Resource Optimization in Heterogeneous Mega-Constellation Networks[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11208547/)

* Chan K L, Chang R Y, Chien F T, et al. Beamforming and Load-Balanced User Association in RIS-Aided mmWave Systems via Adaptive Attention Graph Neural Networks[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11212829/)

* Liu X, Fischione C. Coordinated Beamforming for Multi-cell ISAC using Graph Neural Networks[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11212819/)

* Zhao L, Wu D, He K, et al. Cost-Efficient Federated Learning in Massive IoT: A Physics-Inspired Graph Learning Approach[J]. IEEE Transactions on Communications, 2025, 74: 1019-1032. [Link](https://ieeexplore.ieee.org/abstract/document/11251352/)

* Jiang Y, Hu J, Min G. D2D-Assisted Hierarchical Federated Learning With Clustering Based on Graph Convolutional Networks[J]. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11151783/)

* Bukhari S M A H, Afaq M, Song W C. G-CSL: A GNN-based client-server-link prediction for video streaming in SDN[J]. Journal of Communications and Networks, 2025, 27(6): 521-533. [Link](https://ieeexplore.ieee.org/abstract/document/11333398/)

* Yin B, Schampheleer J, Joseph W, et al. Graph Neural Network Based Energy-Efficient Optimization for RIS-Assisted Wireless Networks[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11078777/)

* Zhang B, Gao R, Gao P, et al. Interference-Suppressed Joint Channel and Power Allocation for Downlinks in Large-Scale Satellite Networks: A Dynamic Hypergraph Neural Network Approach[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11080232/)

* Lian L, Bai C, Xu Y, et al. Learning to Beamform for Cooperative Localization and Communication: A Link Heterogeneous GNN-Based Approach[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/document/11220255)

* Ye M, Zhang J, Guo Z, et al. Learning-Based Adaptive Range Routing for Traffic Engineering With Graph Neural Networks[J]. IEEE Transactions on Networking, 2025, 34: 767-782. [Link](https://ieeexplore.ieee.org/abstract/document/11230331/)

* Li R, Wang S, Huang H, et al. Multi-Dimensional Spectrum Prediction Using Closed-Form Continuous-Time Neural Network With Graph Attention[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11098610/)

* Li Y, Liu Y, Yu W. Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11062505/) [Code](https://github.com/Leeyyhh/Multimodal-Based-User-Association-and-Beamforming)

* Bilen T. Real-Time Congestion Management in 6G Networks via GNN-Based Detection and Queue-Aware Mitigation[J]. Computer Networks, 2025: 111941. [Link](https://www.sciencedirect.com/science/article/pii/S1389128625009065)

* Zhong L, Zhang J, Chen Z, et al. Subtopology-Assisted Federated Graph Learning With Adaptive Neighbor Generation in Edge-Client Collaborative Networks[J]. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11195765/)

* Xie Y, Ding Z, Dai X. Beam Allocation in THz-NOMA Networks: A Graph Neural Network Approach[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11054292/)

* Wu Y, Liu Y, Wang F, et al. Exploring Spatio-Temporal Dynamics for Spectrum Sensing: A GCN-LSTM Approach[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11054296/)

* Zhang H, Huang K, Yang C, et al. Fast Adversarial Training For Graph Neural Network Based Resource Allocation[J]. IEEE Wireless Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11204513/)

* Liu Z, Zhang J, Xu B, et al. GCN-based low-complexity downlink beamforming for cell-free massive MIMO systems with partially coherent joint transmission[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11045790/)

* Mughal U, Elshazly A, Atat R, et al. Generalizable Topology-Aware GNN-Based Intrusion Detection System for UAV Swarms[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/document/11232498/)

* Ma Y, Zhang J, Liu Z, et al. Joint Power Control and Precoding for Cell-Free Massive MIMO Systems With Sparse Multi-Dimensional Graph Neural Networks[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11071383/)

* Zhou H, Xia W, Zheng G, et al. Graph Neural Network-based Continual Learning for Resource Allocation in Dynamic Wireless Environments[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11062595/)

* Gonzalez Bermudez A, Farreras M, Groshev M, et al. Graph Neural Networks for Open Radio Access Network Mobility Management: A Link Prediction Approach[J]. IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2025. [Link](https://ieeexplore.ieee.org/document/11241210/)

* Yang K, Xiao Q, and Wang C. Graph Reinforcement Learning-Based Transmission Scheduling for Underwater Acoustic Networks[J]. IEEE Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/document/11278060/)

* Long K, Chen S, and Xia G. Interference-Aware GCN-Enabled Hybrid Precoding With Limited Feedback: A Deep Joint Optimization Paradigm for FDD Massive MIMO[J]. IEEE Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/document/11259088/)

* Tang H, Zhang J, Zhao Z, et al. Joint Optimization based on Two-phase GNN in RIS-and DF-assisted MISO Systems with Fine-grained Rate Demands[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11030218/)

* Bai Y, Yu D, Zhang X, et al. Link Prediction of UAV Networks Based on Dynamic Graph Neural Network[J]. IET Communications, 2025, 19(1): e70092. [Link](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cmu2.70092)

* Xiao Z, Liu S, Lu H, et al. Reliability Evaluation for WSNs Based on Deep Reinforcement Learning and Graph Neural Networks[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11114373/)

* Perez-Ramirez D F, Pérez-Penichet C, Tsiftes N, et al. Robust Generalization of Graph Neural Networks for Carrier Scheduling[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2025. [Link](https://ieeexplore.ieee.org/document/11271344/) [Code](https://git.ri.se/daniel.perez/robustgantt_scheduler)

* Kumar R, Rathinam M. An ERAN-Based Dynamic Graph Neural Network for CSI Prediction in Massive MIMO Systems[J]. IEEE Wireless Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11124202/)

* Liu Y, Hua J, Zhou B, et al. BI-TE: achieving GNN-based bandwidth indistinguishable traffic engineering in multi-domain SDN[J]. Frontiers of Computer Science, 2025, 19(11): 1911502. [Link](https://link.springer.com/article/10.1007/s11704-024-40551-2)

* Shu Z, Deng X, Zhang J, et al. Decoupled Uplink-Downlink Multi-Connectivity Scheduling in Full-Duplex Cell-Free Massive MIMO Networks: A HGN-DRL Approach[J]. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10979505/)

* Rao Z, Zhu Z, Niyato D, et al. Dynamic LEO Satellite Routing Approach Based on Deep Graph Attention and Incremental Evolutionary Reinforcement Learning[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11153769/)

* Sivanantham E, Sekhar J C, Pullarao M V, et al. Energy‐Efficient Cluster Head Selection and Multipath Routing in WSN Using Heterogeneous‐Hyper Graph Neural Network and Velocity Paused Particle Swarm Optimization[J]. International Journal of Communication Systems, 2026, 39(1): e70263. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70263)

* Cheng X, Liu B, Liu X, et al. Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration[J]. IEEE Transactions on Network Science and Engineering, 2025. [Link](https://ieeexplore.ieee.org/document/11074348)

* Tian K, Yue C, She C, et al. GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach[J]. IEEE Transactions on Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10980232/) [Code](https://github.com/KouTian1999/GNN-DRL-Auto-encoder)

* Feng H, Zhang W, Liu Y, et al. GNN-enabled Multi-Agent DRL for Adaptive Path Selection in Multi-Network Domains[J]. IEEE Transactions on Network Science and Engineering, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11048425/)

* He C, Lu Y, Chen W, et al. Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11004433/)

* Le H A, Van Chien T, Choi W. Graph Neural Network based Active and Passive Beamforming for Distributed STAR-RIS-Assisted Multi-User MISO Systems[J]. IEEE Transactions on Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10955224/)

* Zhang H, Han Y, Meng L, et al. MFFGCN: Multimodal Feature Fusion Graph Convolution Network for Radio Map Estimation With Uneven Spatial Sampling[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11084851/)

* Ba X, Zhang X, Li S, et al. Multimodal semantic communication system based on graph neural networks[J]. Intelligence & Robotics, 2025, 5(3): 805-26. [Link](https://www.oaepublish.com/articles/ir.2025.41)

* He X, Dai Y, Yu C, et al. Multi-User Parallel Channel Extrapolation Based on Graph-Transformer[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11025189/)

* Guo Z, Li F, Xie T, et al. NetGenius: Routing Configuration Recommendation Based on Graph Neural Network[J]. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11031227/)

* Huang X, Huang T, Cheng P, et al. Optimizing Task Migration for Public and Private Services in Vehicular Edge Networks: A Dual-Layer Graph Neural Network Approach[J]. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11080329/)

* Fang H, Huang K, Ye H, et al. Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach[J]. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11027480/)

* Zong Y, Chen L, Canyelles-Pericas P, et al. Resilient time synchronisation for aerial swarms by distributed graph neural networks[J]. IEEE Transactions on Network Science and Engineering, 2025: 1-15. [Link](https://research.utwente.nl/en/publications/resilient-time-synchronisation-for-aerial-swarms-by-distributed-g/) [Code](https://github.com/zongyan/GNNSync)

* Zhang S, Liu A, Han C, et al. Federated Graph Reinforcement Learning-Driven Adaptive Routing Strategy for Mega LEO Satellite Networks[J]. IEEE Wireless Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11099543/)

* Tong R, Guo S, Qiu X, et al. GCN and DRL based on dependent task offloading mechanism in edge computing[J]. Digital Communications and Networks, 2025. [Link](https://www.sciencedirect.com/science/article/pii/S2352864825001488)

* Zhang Y, Li S, Li D, et al. GCN-Based Multi-Station Collaborative Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks[J]. IEEE Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11104132/)

* Feng F, Zhang Y, Wang Z, et al. GOMOS: A GNN-Assisted Network-Media Integrated Optimization Framework for Massive Mobile Live Streaming[J]. IEEE Transactions on Cognitive Communications and Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10829661/)

* Tang X, Zhao K, Shen C, et al. Graph Attention Network-Driven Hierarchical Learning for Anti-Jamming UAV Communications[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11203882/)

* Huang Y, Lin L, Wang X, et al. Graph Neural Network-Based Intermittent Fault Diagnosis for Reliability of Symbiotic Internet of Things[J]. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11028903/)

* Li Y, Lu Y, Zhang G, et al. Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems[J]. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10982443/)

* Ma Z, Zhang N, Zhang H, et al. Joint Design of Sub-channel Assignment and Power Control in D2D Aided Cellular System: A Novel GNN and DRL based Approach[J]. Computer Networks, 2025: 111708. [Link](https://www.sciencedirect.com/science/article/pii/S1389128625006747)

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### Conference

* Tasdighi A, Yousefi M. Adaptive Learned Message Passing Algorithms for Decoding Error Correcting Codes[C]//European WIRELESS 2025; 30th European Wireless Conference. VDE, 2025: 115-120. [Link](https://ieeexplore.ieee.org/abstract/document/11442739/)

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* Zhang J, Xu L, Li R, et al. HetRGNN-MARL: A Graph-Enhanced Multi-Agent Reinforcement Learning Approach for Resource Allocation and Trajectory Optimization in UAV-Assisted ISAC Networks[C]//2025 Seventeenth International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2025: 1-6. [Link](https://ieeexplore.ieee.org/abstract/document/11352329/)

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* Zhang J, Huang H, Shi C, et al. Spatiotemporal-Aware Resilient Routing Algorithm Based on Graph Attention Double DQN[C]//2025 Seventeenth International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2025: 1-6. [Link](https://ieeexplore.ieee.org/abstract/document/11352231/)

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### Preprint

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## 2024

### Journal

* Wang D, Bai Y, Song B. A knowledge graph-based reinforcement learning approach for cooperative caching in MEC-enabled heterogeneous networks[J]. Digital Communications and Networks, 2024. [Link](https://www.sciencedirect.com/science/article/pii/S235286482400172X)

* Du Y, Qi N, Li X, et al. Distributed multi-UAV trajectory planning for downlink transmission: A GNN-enhanced DRL approach[J]. IEEE Wireless Communications Letters, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10716552/)

* Guo X, Xu Y, Sun J, et al. Dynamic Graph Temporal-Frequency Dual-Channel Network for Multi-band Spectrum Prediction[J]. IEEE Communications Letters, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10679647/)

* Khan S, Avgeris M, Gascon-Samson J, et al. EMDTORA: Energy-Aware Multi-User Dependent Task Offloading and Resource Allocation in MEC Using Graph-Enabled DRL[J]. IEEE Transactions on Green Communications and Networking, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10787266/)

* Latif-Martínez H, Suárez-Varela J, Cabellos-Aparicio A, et al. GAT-AD: Graph Attention Networks for contextual anomaly detection in network monitoring[J]. Computers & Industrial Engineering, 2024: 110830. [Link](https://www.sciencedirect.com/science/article/pii/S0360835224009525)

* Dai M, Gong T, Zhang S. Graph Convolutional Network-Based Channel Extrapolation for Hybrid RIS-Aided Communication[J]. IEEE Wireless Communications Letters, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10713240/)

* Liu Z, Zhang J, Shi E, et al. Graph neural network meets multi-agent reinforcement learning: Fundamentals, applications, and future directions[J]. IEEE Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10638531/)

* Hao X, She C, Yeoh P L, et al. Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10745152/)

* Messinis S, El Marai O, Protonotarios N E, et al. Roads Digital Twin: Predictive Situational Awareness Using 360° Video Streaming and Graph Neural Networks[J]. IEEE Transactions on Vehicular Technology, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10783091/)

* Al-Absi G A, Fang Y, Qaseem A A. STC-GraphFormer: Graph Spatial-Temporal Correlation Transformer for In-vehicle Network Intrusion Detection System[J]. Vehicular Communications, 2024: 100865. [Link](https://www.sciencedirect.com/science/article/pii/S2214209624001402)

* Wang Q, Wang X, Liu H, et al. A Domain Adaptive IoT Intrusion Detection Algorithm Based on GWR-GCN Feature Extraction and Conditional Domain Adversary[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10677546/)

* Xiaoyu Z, Peihan Q, Qi L, et al. A graph-based semi-supervised approach for few-shot class-incremental modulation classification[J]. China Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10495864/)

* Dai Y, Lyu L, Cheng N, et al. A Survey of Graph-Based Resource Management in Wireless Networks-Part I: Optimization Approaches[J]. IEEE Transactions on Cognitive Communications and Networking, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10772404/)

* Dai Y, Lyu L, Cheng N, et al.  A Survey of Graph-Based Resource Management in Wireless Networks-Part II: Learning Approaches[J]. IEEE Transactions on Cognitive Communications and Networking, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10771784/)

* Dai B, Li H, Huang W. Distributed on-demand Routing Algorithm with Graph Representation Learning for Industrial IoT[J]. IEEE Transactions on Network Science and Engineering, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10753084/)

* Sun H, Liao X, Wang J, et al. Fast and Scalable ACL Policy Solving Under Complex Constraints With Graph Neural Networks[J]. IEEE/ACM Transactions on Networking, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10551736/)

* Chen D, Zhang W, Gao D, et al. GFlow: GNN-Based Optimal Flow Scheduling for Multipath Transmission with Link Overlapping[J]. IEEE Transactions on Network Science and Engineering, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10720146/)

* Luo J, Fei Z, Wang X, et al. GNN Based Resource Allocation for Digital Twin-Enhanced Multi-UAV Radar Networks[J]. IEEE Wireless Communications Letters, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10669601/)

* Wu H, Tian L, Tang H, et al. Graph Convolutional Reinforcement Learning-Guided Joint Trajectory Optimization and Task Offloading for Aerial Edge Computing[J]. IEEE Transactions on Intelligent Transportation Systems, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10753300/)

* Jiang W, Han H, Zhang Y, et al. Graph Neural Networks for Routing Optimization: Challenges and Opportunities[J]. Sustainability, 2024, 16(21): 9239. [Link](https://www.mdpi.com/2071-1050/16/21/9239)

* Huang Z, Yu F R, Cai J. Knowledge Collaboration-Based Resource Allocation in 6G IoT: A Graph Attention RL Approach[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10560516/)

* Jie Y, Jingchao H, Nan C, et al. Multilayer satellite network collaborative mobile edge caching: A GCN-based multi-agent approach[J]. China Communications, 2024, 21(11): 56-74. [Link](https://ieeexplore.ieee.org/abstract/document/10771949/)

* Xu A, Hu Z, Li X, et al. TransEdge: Task Offloading with GNN and DRL in Edge Computing-Enabled Transportation Systems[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10637671/)

* Hu H, Xie Z, Shi H, et al. Unsupervised Power Allocation Based on Combination of Edge Aggregated Graph Attention Network with Deep Unfolded WMMSE[J]. IEEE Transactions on Vehicular Technology, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10603410/) [Code](https://github.com/xiezhefei/UPAR)

* Shen J, Lin Y, Zhang Y, et al. Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention Reinforcement Learning[J]. IEEE Transactions on Vehicular Technology, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10715675/)

* Kim J, Cho Y, Kim J. Edge Convolution Graph Neural Network assisted Power Allocation for Wireless IoT Networks[J]. IEEE Access, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10676980/)

* Pan Y, Wang X, Xu Z, et al. GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Network[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10643550/) [Code](https://github.com/UNIC-Lab/Qedgix)

* Wang S, Zhang S, Ma J, et al. Graph Neural Network-Based WiFi Indoor Localization System With Access Point Selection[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10601173/)

* Zhang S, Liu A, Han C, et al. GRLR: Routing With Graph Neural Network and Reinforcement Learning for Mega LEO Satellite Constellations[J]. IEEE Transactions on Vehicular Technology, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10701016/)

* Li X, Zhang S, Li H, et al. RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/document/10682510)

* Wan S, Wang Z, Zhou Y. Scalable Hybrid Beamforming for Multi-User MISO Systems: A Graph Neural Network Approach[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10542657/)

* Mehrabian A, Wong V W S. Joint Spectrum, Precoding, and Phase Shifts Design for RIS-Aided Multiuser MIMO THz Systems[J]. IEEE Transactions on Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10472941) [Code](https://github.com/Ali-Meh619/MHGphormer)

* Ma H, Gu Y, Wu H, et al. A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks[J]. Physical Communication, 2024, 67: 102485. [Link](https://www.sciencedirect.com/science/article/pii/S1874490724002039)

* Wang X, Guan K, He D, et al. Graph Neural Network enabled Propagation Graph Method for Channel Modeling[J]. IEEE Transactions on Vehicular Technology, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10484975/)

* Jiang Q, Xu X, Bilal M, et al. Potential game based distributed IoV service offloading with graph attention networks in mobile edge computing[J]. IEEE Transactions on Intelligent Transportation Systems, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10462033/)

* Yonghui H, Zuodong J, Peng Q, et al. Task offloading and resource allocation in NOMA-VEC: A multi-agent deep graph reinforcement learning algorithm[J]. China Communications, 2024, 21(8): 79-88. [Link](https://ieeexplore.ieee.org/abstract/document/10670133/)

* Geng Z, She C, Wang R, et al. Zero-Shot Learning for Beam Management in LEO Satellite Communications[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10516308/)

* Esmaeili B, Azmoodeh A, Dehghantanha A, et al. A GNN-Based adversarial internet of things Malware Detection Framework for critical infrastructure: Studying Gafgyt, Mirai and Tsunami campaigns[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10194255/) [Code](https://github.com/CyberScienceLab/Our-Papers/tree/main/GNN-Adversarial-Detection)

* Li F, Lin J, Wang Y, et al. Distributed Hierarchical Temporal Graph Learning for Communication-Efficient High-Dimensional Industrial IoT Modeling[J]. IEEE Internet of Things Journal, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10533257/)

* Tran D H, Park M. FN-GNN: A Novel Graph Embedding Approach for Enhancing Graph Neural Networks in Network Intrusion Detection Systems[J]. Applied Sciences, 2024, 14(16): 6932. [Link](https://www.mdpi.com/2076-3417/14/16/6932)

* Ahmadi M, Moayyedi A, Sulaiman M, et al. Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation[J]. IEEE Transactions on Network and Service Management, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10620027/)

* Li Y, Lu Y, Ai B, et al. GNN-based beamforming for sum-rate maximization in MU-MISO networks[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10430085/)

* Wang Z, Bennis M, Zhou Y. Graph Attention-based MADRL for Access Control and Resource Allocation in Wireless Networked Control Systems[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10632058/)

* Gu Y, She C, Bi S, et al. Graph Neural Network for Distributed Beamforming and Power Control in Massive URLLC Networks[J]. IEEE Transactions on Wireless Communications, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10419173/) [Code](https://github.com/Yifan-Gu-SZU/G4U-GNN-for-mURLLC)

* Kumar G R S, Arul Geetha G. Graph neural networks based queuing model for optimal load balancing in mobile ad hoc network[J]. International Journal of Communication Systems, e5922, 2024. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5922)

* Wu Y, Liu J, Wang C, et al. Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFC[J]. IEEE Transactions on Network and Service Management, 2024. [Link](https://ieeexplore.ieee.org/abstract/document/10536017/)

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### Conference

* Zheng B, Liang H, Ling J, et al. Integrating Graph Neural Networks with Multi-Agent Deep Reinforcement Learning for Dynamic V2X Communication[C]//2024 20th International Conference on Mobility, Sensing and Networking (MSN). IEEE Computer Society, 2024: 398-405. [Link](https://www.computer.org/csdl/proceedings-article/msn/2024/160200a398/27O9poGVU2c)

* He Y, Xiao G, Liang Y, et al. A graph reinforcement learning routing scheme based on nodes and edges co-embedding[C]//ICC 2024-IEEE International Conference on Communications. IEEE, 2024: 5431-5436. [Link](https://ieeexplore.ieee.org/abstract/document/10623011/)

* Yuan Y, Lei D, Zhang C, et al. Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments[C]//ICC 2024-IEEE International Conference on Communications. IEEE, 2024: 4632-4637. [Link](https://ieeexplore.ieee.org/abstract/document/10622439/)

* Mehrabian A, Wong V W S. Adaptive bandwidth allocation in multiuser MIMO THz systems with graph-transformer networks[C]//ICC 2024-IEEE International Conference on Communications. IEEE, 2024: 367-372. [Link](https://ieeexplore.ieee.org/abstract/document/10622400/)

* Campeny-Roig E, Castell-Uroz I, Barlet-Ros P. AST-GNN: A Graph Neural Network for Web Tracking Detection[C]//Proceedings of the 3rd GNNet Workshop on Graph Neural Networking Workshop. 2024: 27-32. [Link](https://dl.acm.org/doi/abs/10.1145/3694811.3697816) [Code](https://github.com/CBA-UPC/AST-GNN)

* Chan K L, Chien F T, Chang R Y. Beamforming and Load-Balanced User Association in RIS-Aided Systems via Graph Neural Networks[C]//ICC 2024-IEEE International Conference on Communications. IEEE, 2024: 4997-5002. [Link](https://ieeexplore.ieee.org/abstract/document/10622705/)

* Shibli A, Zanouda T. Data-driven radio environment map estimation using graph neural networks[C]//2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024: 650-655. [Link](https://ieeexplore.ieee.org/
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