【论文推荐】GNN for Communication Networks最新论文

2023年开始的一个新系列，主要是整理最新发表的Graph Neural Networks for Communication Networks (GNN4COMM)相关的最新文献，按月更新。这一期一共包括18篇期刊、17篇会议和2篇预印论文，其中9篇论文提供了开源代码，希望能帮助到大家。更多的GNN4COMM相关论文可以查看我们的Github工程(https://github.com/jwwthu/GNN-Communication-Networks)。获取这些论文的全文可以在公众号回复20230531，仅供大家交流学习。欢迎转发和关注！

Journal

• Chen G, Liu Y, Zhang T, et al. A Graph Neural Network based Radio Map Construction Method for Urban Environment[J]. IEEE Communications Letters, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10078269/

• Xu G, Xu M, Chen Y, et al. A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic[J]. Electronics, 2023, 12(10): 2313.
  链接：
  https://www.mdpi.com/2079-9292/12/10/2313

• Fu N, Cheng G, Su X. Accurate compressed traffic detection via traffic analysis using Graph Convolutional Network based on graph structure feature[J]. Computer Communications, 2023.
  链接：
  https://www.sciencedirect.com/science/article/pii/S0140366423001500

• Zhang Y, Zhang M, Gui Y, et al. Adaptive graph convolutional recurrent neural networks for system-level mobile traffic forecasting[J]. China Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10122820/

• Zhao H, Yang B, Cui J, et al. Effective Fault Scenario Identification for Communication Networks Via Knowledge-Enhanced Graph Neural Networks[J]. IEEE Transactions on Mobile Computing, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10113753/

• Asheralieva A, Niyato D, Miyanaga Y. Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks[J]. IEEE Transactions on Mobile Computing, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10083276/

• Ding S, Wang J, Fu X. GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework[J]. IEEE Transactions on Network Science and Engineering, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10102310/

• Deng X, Sun J, Lu J. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing[J]. Sensors, 2023, 23(10): 4936.
  链接：
  https://www.mdpi.com/1424-8220/23/10/4936

• Bilot T, El Madhoun N, Al Agha K, et al. Graph Neural Networks for Intrusion Detection: A Survey[J]. IEEE Access, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10123384/

• Lu P, Jing C, Zhu X. GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks[J]. Processes, 2023, 11(4): 1255.
  链接：
  https://www.mdpi.com/2227-9717/11/4/1255

• Yumlembam R, Issac B, Jacob S M, et al. Iot-based android malware detection using graph neural network with adversarial defense[J]. IEEE Internet of Things Journal, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9814995/

• Zhang Z, Tao M, Liu Y F. Learning to Beamform in Joint Multicast and Unicast Transmission with Imperfect CSI[J]. IEEE Transactions on Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10058567/

• Li Z, Wang X, Pan L, et al. Network Topology Optimization via Deep Reinforcement Learning[J]. IEEE Transactions on Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10045737/

• Yang S, Zhang L, Cui L, et al. RLCS: Towards a robust and efficient mobile edge computing resource scheduling and task offloading system based on graph neural network[J]. Computer Communications, 2023, 206: 38-50.
  链接：
  https://www.sciencedirect.com/science/article/pii/S0140366423001391

• Li D, Zhang H, Ding H, et al. User Preference Learning-based Proactive Edge Caching for D2D-Assisted Wireless Networks[J]. IEEE Internet of Things Journal, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10044187/
  代码：
  https://github.com/lidongyang1/CS-UPL

• Shen Y, Zhang J, Song S H, et al. Graph neural networks for wireless communications: From theory to practice[J]. IEEE Transactions on Wireless Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9944643
  代码：
  https://github.com/yshenaw/GNN4Com

• Mohsenivatani M, Ali S, Ranasinghe V, et al. Graph Representation Learning for Wireless Communications[J]. IEEE Communications Magazine, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10103772/

• Li Y, Lu Y, Zhang R, et al. Deep Learning for Energy Efficient Beamforming in MU-MISO Networks: A GAT-based Approach[J]. IEEE Wireless Communications Letters, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10108002/
  代码：
  https://github.com/GodeWithWind/GNN4SCMU

Conference

• Chen Y, Cao H, Zhou Y, et al. A GCN-GRU Based End-to-End LEO Satellite Network Dynamic Topology Prediction Method[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10118772/

• Pang B, Fu Y, Ren S, et al. A Multi-Modal Approach For Context-Aware Network Traffic Classification[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10095124/

• Zhao Z, Radojicic B, Verma G, et al. Delay-Aware Backpressure Routing Using Graph Neural Networks[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10095267/
  代码：
  https://github.com/zhongyuanzhao/dutyBP

• Abouamer M S, Mitran P. Flexible Resource Allocation in IRS-assisted Systems using Hypernetworks[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10119052/

• Lu X, Zhang X, Lio P. GAT-DNS: DNS Multivariate Time Series Prediction Model Based on Graph Attention Network[C]//Companion Proceedings of the ACM Web Conference 2023. 2023: 127-131.
  链接：
  https://dl.acm.org/doi/abs/10.1145/3543873.3587329

• He H, Kosasih A, Yu X, et al. GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10118792/

• Mo C T, Chen J H, Liao W. Graph Convolutional Network Augmented Deep Reinforcement Learning for Dependent Task Offloading in Mobile Edge Computing[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10119034/

• Wang G, Cheng P, Chen Z, et al. Inverse Reinforcement Learning with Graph Neural Networks for IoT Resource Allocation[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10096237/

• Xu X, Chen H, Simsarian J E, et al. Optical Network Diagnostics Using Graph Neural Networks and Natural Language Processing[C]//Optical Fiber Communication Conference. Optica Publishing Group, 2023: M3G. 5.
  链接：
  https://opg.optica.org/abstract.cfm?uri=OFC-2023-M3G.5

• Chen X, Chuai G, Zhang K, et al. Spatial-temporal Cellular Traffic Prediction: A Novel Method Based on Causality and Graph Attention Network[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10118616/

• Shrestha S, Fu X, Hong M. Towards efficient and optimal joint beamforming and antenna selection: A machine learning approach[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10096331/
  代码：
  https://github.com/XiaoFuLab/Antenna-Selection-and-Beamforming-with-BandB-and-ML

• Xie Z, Xu H, Chen W, et al. Unsupervised Anomaly Detection on Microservice Traces through Graph VAE[C]//Proceedings of the ACM Web Conference 2023. 2023: 2874-2884.
  链接：
  https://dl.acm.org/doi/10.1145/3543507.3583215
  代码：
  https://doi.org/10.5281/zenodo.7197839

• Liu M, Huang C, Di Renzo M, et al. Cooperative Beamforming and RISs Association for Multi-RISs Aided Multi-Users MmWave MIMO Systems through Graph Neural Networks[C]. ICC, 2023.
  链接：
  https://arxiv.org/abs/2302.04183

• Zhang H, Yu L, Xiao X, et al. TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification[C]//Proceedings of the ACM Web Conference 2023. 2023: 2066-2075.
  链接：
  https://dl.acm.org/doi/abs/10.1145/3543507.3583227

• Abode D, Adeogun R, Berardinelli G. Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach[C]//2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10118984/
  代码：
  1https://github.com/danieloaAAU/Power_Control_GNN.git

• He H, Su L, Ye K. GraphGRU: A Graph Neural Network Model for Resource Prediction in Microservice Cluster[C]//2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2023: 499-506.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10077915/
  代码：
  https://github.com/GraphGRU/GraphGRU

• Cheng P, Chen G, Han Z. Graph Neural Networks based Resource Allocation in Heterogeneous Wireless Networks[C]//Proceedings of the 7th International Conference on Intelligent Information Processing. 2022: 1-6.
  链接：
  https://dl.acm.org/doi/abs/10.1145/3570236.3570293

Preprint

• Chen L, Zhu J, Evans J. Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks[J]. arXiv preprint arXiv:2305.12639, 2023.
  链接：
  https://arxiv.org/abs/2305.12639

• Li J, Ye M, Huang L, et al. An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning[J]. arXiv preprint arXiv:2305.10441, 2023.
  链接：
  https://arxiv.org/abs/2305.10441
  代码：
  https://github.com/GuetYe/DRL-PPONSA