{"id":49382173,"url":"https://github.com/jwwthu/GNN-Communication-Networks","last_synced_at":"2026-05-31T06:00:36.066Z","repository":{"id":41824809,"uuid":"353281258","full_name":"jwwthu/GNN-Communication-Networks","owner":"jwwthu","description":"This is the repository for the collection of Graph-based Deep Learning for Communication Networks.","archived":false,"fork":false,"pushed_at":"2026-01-31T08:02:10.000Z","size":963,"stargazers_count":573,"open_issues_count":1,"forks_count":86,"subscribers_count":19,"default_branch":"main","last_synced_at":"2026-02-14T16:29:23.464Z","etag":null,"topics":["commmunication-networks","graph","graph-convolutional-networks","graph-neural-network"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jwwthu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-03-31T08:23:23.000Z","updated_at":"2026-02-11T06:14:57.000Z","dependencies_parsed_at":"2026-01-31T10:04:42.683Z","dependency_job_id":null,"html_url":"https://github.com/jwwthu/GNN-Communication-Networks","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jwwthu/GNN-Communication-Networks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwwthu%2FGNN-Communication-Networks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwwthu%2FGNN-Communication-Networks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwwthu%2FGNN-Communication-Networks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwwthu%2FGNN-Communication-Networks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jwwthu","download_url":"https://codeload.github.com/jwwthu/GNN-Communication-Networks/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwwthu%2FGNN-Communication-Networks/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33720897,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-31T02:00:06.040Z","response_time":95,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["commmunication-networks","graph","graph-convolutional-networks","graph-neural-network"],"created_at":"2026-04-28T06:00:28.382Z","updated_at":"2026-05-31T06:00:36.057Z","avatar_url":"https://github.com/jwwthu.png","language":null,"funding_links":[],"categories":["Related Lists"],"sub_categories":["Notable GitHub Issues \u0026 Discussions"],"readme":"# GNN-Communication-Networks\nThis is the repository for the collection of Graph-based Deep Learning for Communication Networks.\n\nIf you find this repository helpful, you may consider cite our relevant work:\n* Jianping W, Guangqiu Q, Chunming W, et al. \u003cb\u003eFederated learning for network attack detection using attention-based graph neural networks[J]\u003c/b\u003e. Scientific Reports, 2024, 14(1): 19088. [Link](https://www.nature.com/articles/s41598-024-70032-2)\n* Jiang W. \u003cb\u003eGraph-based Deep Learning for Communication Networks: A Survey[J]\u003c/b\u003e. Computer Communications, 2022, 185:40-54. [Link](https://www.sciencedirect.com/science/article/abs/pii/S0140366421004874)\n\t* For the surveyed studies in different scenarios, you may check [survey.md](https://github.com/jwwthu/GNN-Communication-Networks/blob/main/survey.md)\n* Jiang W, Han H, Zhang Y, et al. \u003cb\u003eGraph Neural Networks for Routing Optimization: Challenges and Opportunities[J]\u003c/b\u003e. Sustainability, 2024, 16(21): 9239. [Link](https://www.mdpi.com/2071-1050/16/21/9239)\n\nAdvertisement: 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。\n\n# Other Surveys\n* He S, Xiong S, Ou Y, et al. \u003cb\u003eAn overview on the application of graph neural networks in wireless networks[J]\u003c/b\u003e. IEEE Open Journal of the Communications Society, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9618652/)\n* Suárez-Varela J, Almasan P, Ferriol-Galmés M, et al. \u003cb\u003eGraph Neural Networks for Communication Networks: Context, Use Cases and Opportunities[J]\u003c/b\u003e. IEEE Network, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9846958/)\n* Tam P, Song I, Kang S, et al. \u003cb\u003eGraph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications[J]\u003c/b\u003e. Electronics, 2022, 11(20): 3371. [Link](https://www.mdpi.com/1893620)\n* Ivanov A, Tonchev K, Poulkov V, et al. \u003cb\u003eGraph-Based Resource Allocation for Integrated Space and Terrestrial Communications[J]\u003c/b\u003e. Sensors, 2022, 22(15): 5778. [Link](https://www.mdpi.com/1424-8220/22/15/5778)\n* Lee M, Yu G, Dai H, et al. \u003cb\u003eGraph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions[J]\u003c/b\u003e. IEEE Wireless Communications, 2022, 29(5): 12-19. [Link](https://ieeexplore.ieee.org/abstract/document/9979700/)\n* Li Y, Xie S, Wan Z, et al. \u003cb\u003eGraph-powered learning methods in the Internet of Things: A survey[J]\u003c/b\u003e. Machine Learning with Applications, 2023, 11: 100441. [Link](https://www.sciencedirect.com/science/article/pii/S2666827022001165)\n* Dong G, Tang M, Wang Z, et al. \u003cb\u003eGraph neural networks in IoT: A survey[J]\u003c/b\u003e. 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) \n\n# Competition\n* Suárez-Varela J, Ferriol-Galmés M, López A, et al. \u003cb\u003eThe graph neural networking challenge: a worldwide competition for education in AI/ML for networks[J]\u003c/b\u003e. ACM SIGCOMM Computer Communication Review, 2021, 51(3): 9-16. [Link](https://dl.acm.org/doi/abs/10.1145/3477482.3477485)\n* Ferriol-Galmés M, Suárez-Varela J, Rusek K, et al. \u003cb\u003eScaling Graph-based Deep Learning models to larger networks[J]\u003c/b\u003e. arXiv preprint arXiv:2110.01261, 2021. [Link](https://arxiv.org/abs/2110.01261)\n\n# Tool\n* Pujol-Perich D, Suárez-Varela J, Ferriol-Galmés M, et al. \u003cb\u003eIGNNITION: fast prototyping of graph neural networks for communication networks[M]\u003c/b\u003e//Proceedings of the SIGCOMM'21 Poster and Demo Sessions. 2021: 71-73. [Link](https://dl.acm.org/doi/abs/10.1145/3472716.3472853)\n* Pujol-Perich D, Suárez-Varela J, Ferriol M, et al. \u003cb\u003eIGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems[J]\u003c/b\u003e. IEEE Network, 2021. [Link](https://arxiv.org/abs/2109.06715v1) [Code](https://ignnition.org/doc/)\n\n# Literature\nThe list would be updated monthly.\n\n## 2026\n### Journal\n* Li Y, Feng L, Zhu Z, et al. \u003cb\u003eEM-GAT: An Edge-enhanced Multi-hop Graph Attention Network for Network Intrusion Detection[J]\u003c/b\u003e. Ad Hoc Networks, 2026: 104292. [Link](https://www.sciencedirect.com/science/article/pii/S1570870526001587)\n* Mittal A, Singh S K. \u003cb\u003eMobility-Aware MIMO Channel Estimation for RIS-Assisted Terahertz Systems via Meta-Learned Spatio-Temporal Graph Neural Networks[J]\u003c/b\u003e. Physical Communication, 2026: 103159. [Link](https://www.sciencedirect.com/science/article/pii/S1874490726001680)\n* Yang P, Zheng T, Zhang S, et al. \u003cb\u003eGraph-Meta-Reinforcement-Learning-Based Algorithm for Task Offloading in Vehicular Edge-Cloud Collaboration[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11219184/)\n* Yuan Y, Shen X, Sun L, et al. \u003cb\u003eKey Nodes Prediction and Cascading Failures Mitigation in Dynamic Traffic UASNs Via a GCN-LSTM Model[J]\u003c/b\u003e. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11319241/)\n* Wang K, Jiang Q, Wu Y, et al. \u003cb\u003eSTATGRAPH: Effective in-vehicle intrusion detection via multi-view statistical graph learning[J]\u003c/b\u003e. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11267138/)\n* Teng M, Li X, Zhang X, et al. \u003cb\u003eGraph-Based Spatiotemporal RL Framework for Sequential Task Offloading in Multi-UAV Systems[J]\u003c/b\u003e. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11264345/)\n* Wang X, Wang Z, Cheng N, et al. \u003cb\u003eGraph Neural Network-Based Multicast Routing for On-Demand Streaming Services in 6G Networks[J]\u003c/b\u003e. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11260947/)\n* Li Y, Xu X, Xu J. \u003cb\u003eAI-Enhanced Hierarchical Routing with Q-learning and Graph Neural Networks for 6G-Enabled Internet of Vehicles[J]\u003c/b\u003e. Computer Networks, 2026: 112206. [Link](https://www.sciencedirect.com/science/article/pii/S1389128626002185)\n* Sharma T, Shieh C S, Forng M H, et al. \u003cb\u003eEnhancing Internet of Things Security: A Federated Learning‐Based Hybrid Model With Graph Neural Networks and Transformers[J]\u003c/b\u003e. International Journal of Communication Systems, 2026, 39(6): e70446. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70446)\n* Rajkumar K, Sivakumar V, Shunmugapriya B, et al. \u003cb\u003eGraph 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]\u003c/b\u003e. International Journal of Communication Systems, 2026, 39(5): e70439. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70439)\n* Zhan C, Liu W, Song K, et al. \u003cb\u003eJoint UAV placement and dependent task offloading in multi-UAV MEC networks: A graph attention enhanced DRL approach[J]\u003c/b\u003e. IEEE Transactions on Mobile Computing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11224636/)\n* Yang H, Pan L, Liu S. \u003cb\u003eLearning reward functions via GNNs for multi-agent task placement in edge-cloud LLM services[J]\u003c/b\u003e. Journal of Network and Computer Applications, 2026: 104457. [Link](https://www.sciencedirect.com/science/article/pii/S1084804526000329)\n* Ramesh B, Guganathan L, Arunachalam K P, et al. \u003cb\u003eNon‐Convolutional Decision Transformer Graph Neural Network for Trust‐Aware Routing in 6G‐Enabled MANET‐IoT Networks[J]\u003c/b\u003e. International Journal of Communication Systems, 2026, 39(6): e70437. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.70437)\n* Ampratwum I, Elhadef M, Nayak A. \u003cb\u003ePredictive Maintenance and Reliability in Intelligent 5G Networks based on Graph Neural Networks[J]\u003c/b\u003e. IEEE Access, 2026. [Link](https://ieeexplore.ieee.org/abstract/document/11433666/)\n* Abbass W, Abbas N, Majeed U. \u003cb\u003ePrevail: A 6G spatio-temporal graph learning framework for traffic vulnerability prediction[J]\u003c/b\u003e. Computer Communications, 2026: 108466. [Link](https://www.sciencedirect.com/science/article/pii/S0140366426000563)\n* Kagi S, Chandra K R, Sreethar S, et al. \u003cb\u003eJoint Power and Delay Optimization Based Resource Allocation in Mu‐MIMO‐OFDM System Using Optimized Enhanced Elman Spiking Sparse Graph Neural Network[J]\u003c/b\u003e. Transactions on Emerging Telecommunications Technologies, 2026, 37(2): e70337. [Link](https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.70337)\n* Xu J, Zhang R, Lin D, et al. \u003cb\u003eSparsity-Resilient QoS Prediction via ε-DP Enhanced Subgraph-Inductive GNNs in Internet of Services[J]\u003c/b\u003e. Computer Networks, 2026: 112085. [Link](https://www.sciencedirect.com/science/article/pii/S1389128626000976)\n* Wu Q, Liu Q, He Y, et al. \u003cb\u003eUGV-Assisted Task Allocation for UAVs: A Heterogeneous Graph Reinforcement Learning Approach[J]\u003c/b\u003e. IEEE Transactions on Services Computing, 2026. [Link](https://ieeexplore.ieee.org/abstract/document/11342382/)\n* Wang Z, Yuan F, Qiu H, et al. \u003cb\u003eLarge-Scale BGP Routing Anomaly Detection Based on Graph Attention Auto-Encoder[J]\u003c/b\u003e. IEEE Transactions on Network and Service Management, 2026, 23: 487-501. [Link](https://ieeexplore.ieee.org/abstract/document/11328038/)\n* Wang C, Dong M, Yuan Y, et al. \u003cb\u003eEnergy-efficient trajectory planning for UAV-assisted communication recovery using multi-agent graph reinforcement learning[J]\u003c/b\u003e. Ad Hoc Networks, 2026, 184: 104145. [Link](https://www.sciencedirect.com/science/article/pii/S1570870526000119)\n* Xu J, Chen C, Dai Q, et al. \u003cb\u003eSparse QoS prediction for cloud services via inductive subgraph pattern aware graph neural network[J]\u003c/b\u003e. Computer Communications, 2026, 248: 108415. [Link](https://www.sciencedirect.com/science/article/pii/S0140366426000058)\n\n### Conference\n* Afrin F, Moghim N, Bouk S H, et al. \u003cb\u003eMulti-scale Graph Neural Network for Low-SNR Wireless Signal Classification[C]\u003c/b\u003e//2026 IEEE 23rd Consumer Communications \u0026 Networking Conference (CCNC). IEEE, 2026: 1-7. [Link](https://ieeexplore.ieee.org/abstract/document/11366565/)\n\n## 2025\n### Journal\n* Du Y, Xu S, Chen G, et al. \u003cb\u003eA GNN-based Distributed Beamforming Design for MIMO Cell-Free ISAC Networks[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11143948/)\n* Li Y, Li J, Yu C, et al. \u003cb\u003eA hierarchical conflict resolution framework with graph transformer-based reinforcement learning for heterogeneous UAV networks[J]\u003c/b\u003e. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11146533/)\n* Ma X, Hu J, Liang S, et al. \u003cb\u003eFederated learning and resource-aware graph neural network for intrusion detection in 6G-IoT driven healthcare system[J]\u003c/b\u003e. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/10977998/)\n* Singh P, Hazarika B, Huang W J. \u003cb\u003eGraph-based Federated Multi-Agent DRL for Semantic and Intent-Aware V2X Communication[J]\u003c/b\u003e. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11186815/)\n* Feys T, Van der Perre L, Rottenberg F. \u003cb\u003eLearning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach[J]\u003c/b\u003e. IEEE Journal of Selected Topics in Signal Processing, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11103722/)\n* Yang J, Li B, Zhang X, et al. \u003cb\u003eA Graph Attention Mechanism-Based Scheme for User Access and Resource Optimization in Heterogeneous Mega-Constellation Networks[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11208547/)\n* Chan K L, Chang R Y, Chien F T, et al. \u003cb\u003eBeamforming and Load-Balanced User Association in RIS-Aided mmWave Systems via Adaptive Attention Graph Neural Networks[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11212829/)\n* Liu X, Fischione C. \u003cb\u003eCoordinated Beamforming for Multi-cell ISAC using Graph Neural Networks[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11212819/)\n* Zhao L, Wu D, He K, et al. \u003cb\u003eCost-Efficient Federated Learning in Massive IoT: A Physics-Inspired Graph Learning Approach[J]\u003c/b\u003e. IEEE Transactions on Communications, 2025, 74: 1019-1032. [Link](https://ieeexplore.ieee.org/abstract/document/11251352/)\n* Jiang Y, Hu J, Min G. \u003cb\u003eD2D-Assisted Hierarchical Federated Learning With Clustering Based on Graph Convolutional Networks[J]\u003c/b\u003e. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11151783/)\n* Bukhari S M A H, Afaq M, Song W C. \u003cb\u003eG-CSL: A GNN-based client-server-link prediction for video streaming in SDN[J]\u003c/b\u003e. Journal of Communications and Networks, 2025, 27(6): 521-533. [Link](https://ieeexplore.ieee.org/abstract/document/11333398/)\n* Yin B, Schampheleer J, Joseph W, et al. \u003cb\u003eGraph Neural Network Based Energy-Efficient Optimization for RIS-Assisted Wireless Networks[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11078777/)\n* Zhang B, Gao R, Gao P, et al. \u003cb\u003eInterference-Suppressed Joint Channel and Power Allocation for Downlinks in Large-Scale Satellite Networks: A Dynamic Hypergraph Neural Network Approach[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11080232/)\n* Lian L, Bai C, Xu Y, et al. \u003cb\u003eLearning to Beamform for Cooperative Localization and Communication: A Link Heterogeneous GNN-Based Approach[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/document/11220255)\n* Ye M, Zhang J, Guo Z, et al. \u003cb\u003eLearning-Based Adaptive Range Routing for Traffic Engineering With Graph Neural Networks[J]\u003c/b\u003e. IEEE Transactions on Networking, 2025, 34: 767-782. [Link](https://ieeexplore.ieee.org/abstract/document/11230331/)\n* Li R, Wang S, Huang H, et al. \u003cb\u003eMulti-Dimensional Spectrum Prediction Using Closed-Form Continuous-Time Neural Network With Graph Attention[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11098610/)\n* Li Y, Liu Y, Yu W. \u003cb\u003eMultimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks[J]\u003c/b\u003e. 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)\n* Bilen T. \u003cb\u003eReal-Time Congestion Management in 6G Networks via GNN-Based Detection and Queue-Aware Mitigation[J]\u003c/b\u003e. Computer Networks, 2025: 111941. [Link](https://www.sciencedirect.com/science/article/pii/S1389128625009065)\n* Zhong L, Zhang J, Chen Z, et al. \u003cb\u003eSubtopology-Assisted Federated Graph Learning With Adaptive Neighbor Generation in Edge-Client Collaborative Networks[J]\u003c/b\u003e. IEEE Transactions on Networking, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11195765/)\n* Xie Y, Ding Z, Dai X. \u003cb\u003eBeam Allocation in THz-NOMA Networks: A Graph Neural Network Approach[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11054292/)\n* Wu Y, Liu Y, Wang F, et al. \u003cb\u003eExploring Spatio-Temporal Dynamics for Spectrum Sensing: A GCN-LSTM Approach[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11054296/)\n* Zhang H, Huang K, Yang C, et al. \u003cb\u003eFast Adversarial Training For Graph Neural Network Based Resource Allocation[J]\u003c/b\u003e. IEEE Wireless Communications Letters, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11204513/)\n* Liu Z, Zhang J, Xu B, et al. \u003cb\u003eGCN-based low-complexity downlink beamforming for cell-free massive MIMO systems with partially coherent joint transmission[J]\u003c/b\u003e. IEEE Transactions on Wireless Communications, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11045790/)\n* Mughal U, Elshazly A, Atat R, et al. \u003cb\u003eGeneralizable Topology-Aware GNN-Based Intrusion Detection System for UAV Swarms[J]\u003c/b\u003e. IEEE Internet of Things Journal, 2025. [Link](https://ieeexplore.ieee.org/document/11232498/)\n* Ma Y, Zhang J, Liu Z, et al. \u003cb\u003eJoint Power Control and Precoding for Cell-Free Massive MIMO Systems With Sparse Multi-Dimensional Graph Neural Networks[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. [Link](https://ieeexplore.ieee.org/abstract/document/11071383/)\n* Zhou H, Xia W, Zheng G, et al. \u003cb\u003eGraph Neural Network-based Continual Learning for Resource Allocation in Dynamic Wireless Environments[J]\u003c/b\u003e. IEEE Transactions on Vehicular Technology, 2025. 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