【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，更多的GNN4COMM相关论文可以查看我们的Github工作(https://github.com/jwwthu/GNN-Communication-Networks)。获取这些论文的全文可以在公众号回复20230228，仅供大家交流学习。欢迎转发和关注！
Journal

• Janu D, Kumar S, Singh K. A Graph Convolution Network Based Adaptive Cooperative Spectrum Sensing in Cognitive Radio Network[J]. IEEE Transactions on Vehicular Technology, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9918025/

• Wang Z, Zhou Y, Zou Y, et al. A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning[J]. IEEE Transactions on Wireless Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10032291/
  代码：
  https://github.com/XiaoWangya/GNNforOTAFL.git

• Diao Z, Xie G, Wang X, et al. EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks[J]. Computer Networks, 2023: 109614.
  链接：
  https://www.sciencedirect.com/science/article/pii/S1389128623000592

• Zhou X, Bilal M, Dou R, et al. Edge Computation Offloading With Content Caching in 6G-Enabled IoV[J]. IEEE Transactions on Intelligent Transportation Systems, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10034418/

• Zhang H, Zeng K, Lin S. Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks[J]. IEEE Transactions on Information Forensics and Security, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10026810/

• Ghasemzadeh P, Hempel M, Wang H, et al. GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification[J]. IEEE Transactions on Wireless Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10032243/

• Zeng L, Yang C, Huang P, et al. GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers[J]. IEEE Journal on Selected Areas in Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9996395/

• Theodoropoulos T, Makris A, Kontopoulos I, et al. Graph neural networks for representing multivariate resource usage: A multiplayer mobile gaming case-study[J]. International Journal of Information Management Data Insights, 2023, 3(1): 100158.
  链接：
  https://www.sciencedirect.com/science/article/pii/S2667096823000058

• Sun Z, Mo Y, Yu C. Graph Reinforcement Learning based Task offloading for Multi-access Edge Computing[J]. IEEE Internet of Things Journal, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9592681/

• Wei W, Fu L, Gu H, et al. GRL-PS: Graph embedding-based DRL approach for adaptive path selection[J]. IEEE Transactions on Network and Service Management, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10029936/

• Zhao X, Wu C. Large-scale Machine Learning Cluster Scheduling via Multi-agent Graph Reinforcement Learning[J]. IEEE Transactions on Network and Service Management, 2022.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9667106

• Liu T, Li Z, Long H, et al. NT-GNN: Network Traffic Graph for 5G Mobile IoT Android Malware Detection[J]. Electronics, 2023, 12(4): 789.
  链接：
  https://www.mdpi.com/2116430

Conference

• Bi F, He T, Luo X. A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation[C]//2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022: 855-860.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10027700/
  代码：
  https://github.com/Oak-B/ICDM-2022-TLGCN

• Wang H, Zhang R, Cheng X, et al. Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network[C]//2022 14th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2022: 221-225.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10039323/

• Ge Z, Hou J, Nayak A. Forecasting SDN End-to-End Latency Using Graph Neural Network[C]//2023 International Conference on Information Networking (ICOIN). IEEE, 2023: 293-298.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10048915/

Preprint

• He H, Yu X, Zhang J, et al. Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems[J]. arXiv preprint arXiv:2302.06896, 2023.
  链接：
  https://arxiv.org/abs/2302.06896
  Journal

• Ren G, Cheng G, Fu N. Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network[J]. Applied Sciences, 2023, 13(3): 1483.
  链接：
  https://www.mdpi.com/2089532

• Chen J, Xiao W, Li X, et al. A routing optimization method for software-defined optical transport networks based on ensembles and reinforcement learning[J]. Sensors, 2022, 22(21): 8139.
  链接：
  https://www.mdpi.com/1424-8220/22/21/8139

• Bernárdez G, Suárez-Varela J, López A, et al. MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering[J]. IEEE Transactions on Cognitive Communications and Networking, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10013773/
  代码：
  https://github.com/BNN-UPC/Papers/wiki/MAGNNETO-TE

• Nikoloska I, Simeone O. Modular meta-learning for power control via random edge graph neural networks[J]. IEEE Transactions on Wireless Communications, 2022, 22(1): 457-470.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9852158/

• Qiu R, Bao J, Li Y, et al. Virtual network function deployment algorithm based on graph convolution deep reinforcement learning[J]. The Journal of Supercomputing, 2022: 1-22.
  链接：
  https://link.springer.com/article/10.1007/s11227-022-04947-w

Conference

• Tonchev K, Neshov N, Ivanov A, et al. Automatic Modulation Classification using Graph Convolutional Neural Networks for Time-frequency Representation[C]//2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2022: 75-79.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10014833/

• Bai Y, Wang D, Song B. A Knowledge Graph-based Cooperative Caching Scheme in MEC-enabled Heterogeneous Networks[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 5959-5964.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001575/

• Kim J, Lee H, Park S H. Autoencoding Graph Neural Networks for Scalable Transceiver Design[C]//2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE, 2022: 1-2.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10012954/

• Su Y, Zhou H, Deng Y. D2D-Based Cellular-Connected UAV Swarm Control Optimization via Graph-Aware DRL[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 1326-1331.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001506/

• Randall M, Belzarena P, Larroca F, et al. Deep Reinforcement Learning and Graph Neural Networks for Efficient Resource Allocation in 5G Networks[C]//2022 IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2022: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10000511/
  代码：
  https://gitlab.fing.edu.uy/mrandall/grows

• Lent R. Dynamic Routing in Challenged Networks with Graph Neural Networks[C]//2022 IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2022: 1-6.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10000566/

• Chen R, Shi H, Wu J, et al. GCNPart: Interference-Aware Resource Partitioning Framework with Graph Convolutional Neural Networks and Deep Reinforcement Learning[C]//Algorithms and Architectures for Parallel Processing: 22nd International Conference, ICA3PP 2022, Copenhagen, Denmark, October 10–12, 2022, Proceedings. Cham: Springer Nature Switzerland, 2023: 568-589.
  链接：
  https://link.springer.com/chapter/10.1007/978-3-031-22677-9_30

• Li N, Iosifidis A, Zhang Q. Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 982-987.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001067/

• Zhu H, Lu J. Graph-based Intrusion Detection System Using General Behavior Learning[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 2621-2626.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001299/

• Li C, Li F, Yu M, et al. Insider Threat Detection Using Generative Adversarial Graph Attention Networks[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 2680-2685.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001207/

• Tonchev K, Ivanov A, Neshov N, et al. Learning Graph Convolutional Neural Networks to Predict Radio Environment Maps[C]//2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2022: 392-395.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10014842/

• Zhang H, Tian Q, Han Y. Multi channel spectrum prediction algorithm based on GCN and LSTM[C]//2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). IEEE, 2022: 1-5.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10013030/

• Zhu R, Luo X, Yao J, et al. Prediction of Cellular Network Channel Utilization Based on Graph Convolutional Networks[C]//2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2022: 1233-1238.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9978155/

• He S, Ou Y, Wang L, et al. Representation Learning of Knowledge Graph for Wireless Communication Networks[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 1338-1343.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001185/

• Li C, Lou J, Liu S, et al. Shapley Explainer-An Interpretation Method for GNNs Used in SDN[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 5534-5540.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10001460/

• Xu H, Li S, Cheng Z, et al. TrafficGCN: Mobile Application Encrypted Traffic Classification Based on GCN[C]//GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 2022: 891-896.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10000658/

• Geng Z, She C, Zhang D, et al. Zero-Shot Recurrent Graph Neural Networks for Beam Prediction in Non-Terrestrial Networks[C]//2022 IEEE Globecom Workshops (GC Wkshps). IEEE, 2022: 1400-1405.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10008701/
  Journal

• Hou J, Lu H, Nayak A. A GNN-based proactive caching strategy in NDN networks[J]. Peer-to-Peer Networking and Applications, 2023: 1-13.
  链接：
  https://link.springer.com/article/10.1007/s12083-023-01464-2

• Zhang X, Zhao H, Wei J, et al. Cooperative Trajectory Design of Multiple UAV Base Stations with Heterogeneous Graph Neural Networks[J]. IEEE Transactions on Wireless Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9892688/
  代码：
  https://github.com/zhangxiaochen95/uav_bs_ctrl

• Schynol L, Pesavento M. Coordinated sum-rate maximization in multicell MU-MIMO with deep unrolling[J]. IEEE Journal on Selected Areas in Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10040241/
  代码：
  https://github.com/lsky96/gcnwmmse

• Deng X, Zhu J, Pei X, et al. Flow Topology-based Graph Convolutional Network for Intrusion Detection in Label-Limited IoT Networks[J]. IEEE Transactions on Network and Service Management, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9919790/

• Fawaz H, Lesca J, Quang P T A, et al. Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents[J]. IEEE Transactions on Network and Service Management, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9969933/

• Gu Y, She C, Quan Z, et al. Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air[J]. IEEE Transactions on Wireless Communications, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10068338/
  代码：
  https://github.com/Yifan-Gu-SZU/GNN-aggregation-over-the-air

• Wang Z, Li Z, Pan H, et al. Large-Scale Measurements and Prediction of DC-WAN Traffic[J]. IEEE Transactions on Parallel and Distributed Systems, 2023, 34(5): 1390-1405.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10045033/
  代码：
  https://github.com/wzhCode/IntegNet

• Liu W, Cai J, Zhu Y, et al. Load Balancing Inside Programmable Data Planes Based on Network Modeling Prediction Using a GNN with Network Behaviors[J]. Computer Networks, 2023: 109695.
  链接：
  https://www.sciencedirect.com/science/article/pii/S1389128623001408

• Chen N, Shen S, Duan Y, et al. Non-Euclidean Graph-Convolution Virtual Network Embedding for Space–Air–Ground Integrated Networks[J]. Drones, 2023, 7(3): 165.
  链接：
  https://www.mdpi.com/2504-446X/7/3/165

• Gao H, Zhu Q, Wang W. Optimal deployment of large-scale wireless sensor networks based on graph clustering and matrix factorization[J]. EURASIP Journal on Advances in Signal Processing, 2023, 2023(1): 1-17.
  链接：
  https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-023-00995-3

• Nerini M, Clerckx B. Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing[J]. IEEE Wireless Communications Letters, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10011630/

• Pramod Kumar P, Sagar K. Reinforcement learning and neuro‐fuzzy GNN‐based vertical handover decision on internet of vehicles[J]. Concurrency and Computation: Practice and Experience, e7688, 2023.
  链接：
  https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.7688

• Gao X, Wang J, Zhou M. The Research of Resource Allocation Method Based on GCN-LSTM in 5G Network[J]. IEEE Communications Letters, 2023.
  链接：
  https://ieeexplore.ieee.org/abstract/document/9961174/

• Lo W W, Kulatilleke G, Sarhan M, et al. XG-BoT: An explainable deep graph neural network for botnet detection and forensics[J]. Internet of Things, 2023: 100747.
  链接：
  https://www.sciencedirect.com/science/article/pii/S2542660523000707

Conference

• Kisanga P, Woungang I, Traore I, et al. Network Anomaly Detection Using a Graph Neural Network[C]//2023 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2023: 61-65.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10074111/

• Chen L, Yan N, Zhang B, et al. A General Backdoor Attack to Graph Neural Networks Based on Explanation Method[C]//2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2022: 759-768.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10063438/

• Boukhtouta A, Madi T, Pourzandi M, et al. Cloud Native Applications Profiling using a Graph Neural Networks Approach[C]//2022 IEEE Future Networks World Forum (FNWF). IEEE, 2022: 220-227.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10056608/

• Yan N, Wen Y, Chen L, et al. Deepro: Provenance-based APT Campaigns Detection via GNN[C]//2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2022: 747-758.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10063343/

• Liu Y, She C, Hardjawana W, et al. Graph Neural Networks for Timely Updates of Short Packets in Interference-Limited Networks[C]//2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022: 1050-1054.
  链接：
  https://ieeexplore.ieee.org/abstract/document/10051893/

Preprint

• Perera T, Atapattu S, Fang Y, et al. Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks[J]. arXiv preprint arXiv:2301.11166, 2023.
  链接：
  https://arxiv.org/abs/2301.11166
  代码：
  https://github.com/tharaka-perera/flex-net

• Xiao B, Li R, Wang F, et al. Stochastic Graph Neural Network-based Value Decomposition for MARL in Internet of Vehicles[J]. arXiv preprint arXiv:2303.13213, 2023.
  链接：
  https://arxiv.org/abs/2303.13213

• Zhao Z, Radojicic B, Verma G, et al. Delay-aware Backpressure Routing Using Graph Neural Networks[J]. arXiv preprint arXiv:2211.10748, 2022.
  链接：
  https://arxiv.org/abs/2211.10748
  代码：
  https://github.com/zhongyuanzhao/dutyBP