/GNN-Communication-Networks

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

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:

  • Jiang W. Graph-based Deep Learning for Communication Networks: A Survey[J]. arXiv preprint arXiv:2106.02533, 2021. Link

Wireless Scenario

General Case

Binary Classification

  • Lee M, Yu G, Dai H. Decentralized Inference with Graph Neural Networks in Wireless Communication Systems[J]. arXiv preprint arXiv:2104.09027, 2021. Link

Channel Allocation

  • Nakashima K, Kamiya S, Ohtsu K, et al. Deep reinforcement learning-based channel allocation for wireless lans with graph convolutional networks[J]. IEEE Access, 2020, 8: 31823-31834. Link

Network Flow Optimization

  • Zhang S, Yin B, Cheng Y. Topology Aware Deep Learning for Wireless Network Optimization[J]. arXiv preprint arXiv:1912.08336, 2019. Link

Power Allocation and Control

  • Eisen M, Ribeiro A. Large scale wireless power allocation with graph neural networks[C]//2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019: 1-5. Link
  • Eisen M, Ribeiro A. Transferable Policies for Large Scale Wireless Networks with Graph Neural Networks[C]//ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020: 5040-5044. Link
  • Eisen M, Ribeiro A. Optimal wireless resource allocation with random edge graph neural networks[J]. IEEE Transactions on Signal Processing, 2020, 68: 2977-2991. Link
  • Nikoloska I, Simeone O. Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks[J]. arXiv preprint arXiv:2105.00459, 2021. Link Code
  • Shen Y, Shi Y, Zhang J, et al. A graph neural network approach for scalable wireless power control[C]//2019 IEEE Globecom Workshops (GC Wkshps). IEEE, 2019: 1-6. Link Code
  • Shen Y, Shi Y, Zhang J, et al. Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis[J]. IEEE Journal on Selected Areas in Communications, 2020, 39(1): 101-115. Link Code
  • Naderializadeh N, Eisen M, Ribeiro A. Wireless power control via counterfactual optimization of graph neural networks[C]//2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2020: 1-5. Link
  • Chowdhury A, Verma G, Rao C, et al. Efficient power allocation using graph neural networks and deep algorithm unfolding[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 4725-4729. Link Code
  • Chowdhury A, Verma G, Rao C, et al. Unfolding wmmse using graph neural networks for efficient power allocation[J]. IEEE Transactions on Wireless Communications, 2021. Link Code

Cellular Network

Channel Estimation

  • Tekbıyık K, Kurt G K, Huang C, et al. Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks[J]. arXiv preprint arXiv:2010.12004, 2020. Link

Idle Time Windows Prediction

  • Fang L, Cheng X, Wang H, et al. Idle time window prediction in cellular networks with deep spatiotemporal modeling[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6): 1441-1454. Link

Integrated Access and Backhaul Topology Design

  • Simsek M, Orhan O, Nassar M, et al. IAB Topology Design: A Graph Embedding and Deep Reinforcement Learning Approach[J]. IEEE Communications Letters, 2020. Link

Network Slicing

  • Wang H, Wu Y, Min G, et al. A Graph Neural Network-based Digital Twin for Network Slicing Management[J]. IEEE Transactions on Industrial Informatics, 2020. Link
  • Shao Y, Li R, Zhao Z, et al. Graph Attention Network-based DRL for Network Slicing Management in Dense Cellular Networks[C]//2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2021: 1-6. Link

Power Control

  • Guo J, Yang C. Learning Power Control for Cellular Systems with Heterogeneous Graph Neural Network[C]//2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2021: 1-6. Link
  • Hou K, Xu Q, Zhang X, et al. User Association and Power Allocation Based on Unsupervised Graph Model in Ultra-Dense Network[C]//2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2021: 1-6. Link

Routing

  • Zhu T, Chen X, Chen L, et al. GCLR: GNN-Based Cross Layer Optimization for Multipath TCP by Routing[J]. IEEE Access, 2020, 8: 17060-17070. Link

Traffic Prediction

  • Sun F, Wang P, Zhao J, et al. Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns[J]. IEEE Transactions on Mobile Computing, 2021. Link
  • He K, Huang Y, Chen X, et al. Graph attention spatial-temporal network for deep learning based mobile traffic prediction[C]//2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019: 1-6. Link
  • He K, Chen X, Wu Q, et al. Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction[J]. IEEE Transactions on Mobile Computing, 2020. Link
  • Pan C, Zhu J, Kong Z, et al. DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting[J]. Electronics, 2021, 10(9): 1014. Link

Virtual Network Embedding

  • Rkhami A, Pham T A Q, Hadjadj-Aoul Y, et al. On the Use of Graph Neural Networks for Virtual Network Embedding[C]//2020 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2020: 1-6. Link
  • Rkhami A, Hadjadj-Aoul Y, Outtagarts A. Learn to improve: A novel deep reinforcement learning approach for beyond 5G network slicing[C]//2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2021: 1-6. Link

Cognitive Radio Network

Resource Allocation

  • Zhao D, Qin H, Song B, et al. A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network[J]. Sensors, 2020, 20(18): 5216. Link

D2D Network

Cooperative Caching and Fetching

  • Yan Y, Zhang B, Li C, et al. Cooperative Caching and Fetching in D2D Communications-A Fully Decentralized Multi-Agent Reinforcement Learning Approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 16095-16109. Link

Power Control and Beamforming

  • Zhang X, Zhao H, Xiong J, et al. Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks[J]. arXiv preprint arXiv:2104.05463, 2021. Link

Wireless Link Scheduling

  • Lee M, Yu G, Li G Y. Graph embedding based wireless link scheduling with few training samples[J]. IEEE Transactions on Wireless Communications, 2020. Link
  • Lee M, Yu G, Li G Y. Wireless Link Scheduling for D2D Communications with Graph Embedding Technique[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6. Link
  • Fu J, Ma N, Ye M, et al. Wireless D2D Network Link Scheduling based on Graph Embedding[C]//2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). IEEE, 1-5. Link

IoT Network

Channel Estimation

  • Tekbıyık K, Kurt G K, Ekti A R, et al. Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications[J]. arXiv preprint arXiv:2104.00735, 2021. Link

Intrusion Detection

  • Lo W W, Layeghy S, Sarhan M, et al. E-GraphSAGE: A Graph Neural Network based Intrusion Detection System[J]. arXiv preprint arXiv:2103.16329, 2021. Link

Service Function Chain Dynamic Reconfiguration

  • Liu Y, Lu Y, Li X, et al. On dynamic service function chain reconfiguration in IoT networks[J]. IEEE Internet of Things Journal, 2020, 7(11): 10969-10984. Link

Satellite Network

Traffic Prediction

  • Yang L, Gu X, Shi H. A Noval Satellite Network Traffic Prediction Method Based on GCN-GRU[C]//2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020: 718-723. Link

Vehicular Network

Communication Latency Modeling

  • Liu J, Xiao Y, Li Y, et al. Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks[J]. arXiv preprint arXiv:2103.07636, 2021. Link

Spectrum Allocation

  • He Z, Wang L, Ye H, et al. Resource Allocation based on Graph Neural Networks in Vehicular Communications[C]//GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE, 2020: 1-5. Link

Wired Scenario

General Case

BGP Configuration Synthesis

  • Bahnasy M, Li F, Xiao S, et al. DeepBGP: A Machine Learning Approach for BGP Configuration Synthesis[C]//Proceedings of the Workshop on Network Meets AI & ML. 2020: 48-55. Link

Botnet Detection

  • Zhou J, Xu Z, Rush A M, et al. Automating Botnet Detection with Graph Neural Networks[C]. AutoML for Networking and Systems Workshop of MLSys 2020 Conference. Link Data

Communication Delay Estimation

  • Suzuki T, Yasuda Y, Nakamura R, et al. On Estimating Communication Delays using Graph Convolutional Networks with Semi-Supervised Learning[C]//2020 International Conference on Information Networking (ICOIN). IEEE, 2020: 481-486. Link

Delay Prediction

  • Rusek K, Chołda P. Message-passing neural networks learn little’s law[J]. IEEE Communications Letters, 2018, 23(2): 274-277. Link

Intrusion Detection

  • Cheng Q, Wu C, Zhou S. Discovering Attack Scenarios via Intrusion Alert Correlation using Graph Convolutional Networks[J]. IEEE Communications Letters, 2021. Link

MPLS Configuration Analysis

  • Geyer F, Schmid S. DeepMPLS: fast analysis of MPLS configurations using deep learning[C]//2019 IFIP Networking Conference (IFIP Networking). IEEE, 2019: 1-9. Link

Network Calculus Analysis

  • Geyer F, Bondorf S. DeepTMA: Predicting effective contention models for network calculus using graph neural networks[C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019: 1009-1017. Link
  • Geyer F, Bondorf S. Graph-based Deep Learning for Fast and Tight Network Calculus Analyses[J]. IEEE Transactions on Network Science and Engineering, 2020. Link
  • Geyer F, Bondorf S. On the robustness of deep learning-predicted contention models for network calculus[C]//2020 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2020: 1-7. Link

Network Configuration Feasibility

  • Mai T L, Navet N. Improvements to Deep-Learning-based Feasibility Prediction of Switched Ethernet Network Configurations[C]//The 29th International Conference on Real-Time Networks and Systems (RTNS2021). 2021. Link

Network Modeling

  • Suárez-Varela J, Carol-Bosch S, Rusek K, et al. Challenging the generalization capabilities of Graph Neural Networks for network modeling[C]//Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos. 2019: 114-115. Link
  • Badia-Sampera A, Suárez-Varela J, Almasan P, et al. Towards more realistic network models based on Graph Neural Networks[C]//Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies. 2019: 14-16. Link
  • Ferriol-Galmés M, Suárez-Varela J, Barlet-Ros P, et al. Applying Graph-based Deep Learning To Realistic Network Scenarios[J]. arXiv preprint arXiv:2010.06686, 2020. Link
  • Geyer F. DeepComNet: Performance evaluation of network topologies using graph-based deep learning[J]. Performance Evaluation, 2019, 130: 1-16. Link

Routing

  • Geyer F, Carle G. Learning and generating distributed routing protocols using graph-based deep learning[C]//Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks. 2018: 40-45. Link
  • Xiao S, Mao H, Wu B, et al. Neural Packet Routing[C]//Proceedings of the Workshop on Network Meets AI & ML. 2020: 28-34. Link

Traffic Prediction

  • Zhao J, Qu H, Zhao J, et al. Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction[J]. Transactions on Emerging Telecommunications Technologies, 2020, 31(11): e4056. Link
  • Yang C, Zhou Z, Wen H, et al. MSTNN: A graph learning based method for the origin-destination traffic prediction[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6. Link
  • Mallick T, Kiran M, Mohammed B, et al. Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks[J]. arXiv preprint arXiv:2008.12767, 2020. Link

Blockchain Platform

Encrypted Traffic Classification

  • Shen M, Zhang J, Zhu L, et al. Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 2367-2380. Link

Datacenter Network

Traffic Optimization

  • Li J, Sun P, Hu Y. Traffic modeling and optimization in datacenters with graph neural network[J]. Computer Networks, 2020, 181: 107528. Link

Optical Network

Resource Allocation

  • Gao Z, Eisen M, Ribeiro A. Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks[J]. arXiv preprint arXiv:2006.15005, 2020. Link

Routing

  • Almasan P, Suárez-Varela J, Badia-Sampera A, et al. Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case[J]. arXiv preprint arXiv:1910.07421, 2019. Link

Traffic Prediction

  • Gui Y, Wang D, Guan L, et al. Optical Network Traffic Prediction Based on Graph Convolutional Neural Networks[C]//2020 Opto-Electronics and Communications Conference (OECC). IEEE, 2020: 1-3. Link

Software Defined Networking Scenario

Network Modeling

  • Rusek K, Suárez-Varela J, Mestres A, et al. Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN[C]//Proceedings of the 2019 ACM Symposium on SDN Research. 2019: 140-151. Link
  • Rusek K, Suárez-Varela J, Almasan P, et al. RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(10): 2260-2270. Link

Routing

  • Zhuang Z, Wang J, Qi Q, et al. Toward greater intelligence in route planning: A graph-aware deep learning approach[J]. IEEE Systems Journal, 2019, 14(2): 1658-1669. Link
  • Sawada K, Kotani D, Okabe Y. Network Routing Optimization Based on Machine Learning Using Graph Networks Robust against Topology Change[C]//2020 International Conference on Information Networking (ICOIN). IEEE, 2020: 608-615. Link

Service Function Chaining

  • Heo D N, Lee D, Kim H G, et al. Reinforcement Learning of Graph Neural Networks for Service Function Chaining[J]. arXiv preprint arXiv:2011.08406, 2020. Link
  • Heo D N, Lange S, Kim H G, et al. Graph Neural Network based Service Function Chaining for Automatic Network Control[C]//2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2020: 7-12. Link
  • Rafiq A, Khan T A, Afaq M, et al. Service Function Chaining and Traffic Steering in SDN using Graph Neural Network[C]//2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020: 500-505. Link
  • Tianfu Wang, Qilin Fan, Xiuhua Li, Xu Zhang, Qingyu Xiong, Shu Fu, and Min Gao, "DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning", In Proc. of IEEE ICC, June 2021, Montreal, CA. Link

Virtual Network Function

  • Sun P, Lan J, Guo Z, et al. DeepMigration: Flow Migration for NFV with Graph-based Deep Reinforcement Learning[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6. Link
  • Sun P, Lan J, Li J, et al. Efficient flow migration for NFV with Graph-aware deep reinforcement learning[J]. Computer Networks, 2020, 183: 107575. Link
  • Habibi F, Dolati M, Khonsari A, et al. Accelerating Virtual Network Embedding with Graph Neural Networks[C]//2020 16th International Conference on Network and Service Management (CNSM). IEEE, 2020: 1-9. Link
  • Yan Z, Ge J, Wu Y, et al. Automatic virtual network embedding: A deep reinforcement learning approach with graph convolutional networks[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(6): 1040-1057. Link
  • Kim H G, Park S, Heo D, et al. Graph Neural Network-based Virtual Network Function Deployment Prediction[C]//2020 16th International Conference on Network and Service Management (CNSM). IEEE, 2020: 1-7. Link
  • Kim H G, Park S, Heo D, et al. Graph Neural Network-based Virtual Network Function Deployment Prediction[C]//2020 16th International Conference on Network and Service Management (CNSM). IEEE, 2020: 1-7. Link
  • Kim H G, Park S, Lange S, et al. Graph neural network-based virtual network function management[C]//2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2020: 13-18. Link
  • Sun P, Lan J, Li J, et al. Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement[J]. IEEE Communications Letters, 2020. Link
  • Jalodia N, Henna S, Davy A. Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments[C]//2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2019: 1-5. Link
  • Mijumbi R, Hasija S, Davy S, et al. A connectionist approach to dynamic resource management for virtualised network functions[C]//2016 12th International Conference on Network and Service Management (CNSM). IEEE, 2016: 1-9. Link
  • Mijumbi R, Hasija S, Davy S, et al. Topology-aware prediction of virtual network function resource requirements[J]. IEEE Transactions on Network and Service Management, 2017, 14(1): 106-120. Link