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]. Computer Communications, 2022, 185:40-54. Link

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
• 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

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
• 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

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
• 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 Code

Wireless Scenario

General Case

Binary Classification

• Lee M, Yu G, Dai H. Decentralized Inference with Graph Neural Networks in Wireless Communication Systems[J]. IEEE Transactions on Mobile Computing, 2021. Link

Channel Allocation

• Yang Y, Zhang S, Gao F, et al. Graph Neural Network-Based Channel Tracking for Massive MIMO Networks[J]. IEEE Communications Letters, 2020, 24(8): 1747-1751. Link
• 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

Channel Prediction

• Li N, Jia S, Li Q. Traffic Message Channel Prediction Based on Graph Convolutional Network[J]. IEEE Access, 2021, 9: 135423-135431. 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

• Guo J, Yang C. Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural networks[J]. IEEE Transactions on Wireless Communications, 2022, 21(2): 884-897. Link
• NaderiAlizadeh N, Eisen M, Ribeiro A. Adaptive Wireless Power Allocation with Graph Neural Networks[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 5213-5217. Link
• Li B, Verma G, Rao C, et al. Energy-Efficient Power Allocation in Wireless Networks using Graph Neural Networks[C]//2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021: 732-736. Link
• Nikoloska I, Simeone O. Black-box and modular meta-learning for power control via random edge graph neural networks[J]. arXiv preprint arXiv:2108.13178, 2021. Link
• Guo J, Yang C. Learning Power Allocation for Multi-cell-multi-user Systems with Heterogeneous Graph Neural Network[J]. IEEE Transactions on Wireless Communications, 2021. Link
• Zhang X, Zhang Z, Yang L. Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning[J]. arXiv preprint arXiv:2103.15367, 2021. Link
• Guo J, Yang C. Learning Power Allocation for Multi-cell-multi-user Systems with Heterogeneous Graph Neural Network[J]. IEEE Transactions on Wireless Communications, 2021. Link
• Li B, Verma G, Segarra S. Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks[J]. arXiv preprint arXiv:2201.11799, 2022. Link Code
• 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

Resource Management

• Wang Z, Eisen M, Ribeiro A. Learning decentralized wireless resource allocations with graph neural networks[J]. IEEE Transactions on Signal Processing, 2022, 70: 1850-1863. Link
• Shen Y, Zhang J, Song S H, et al. AI Empowered Resource Management for Future Wireless Networks[C]. Meditcom 2021. Link

Traffic Prediction

• Zhang K, Zhao X, Li X, et al. Network Traffic Prediction via Deep Graph-Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology[J]. Wireless Communications and Mobile Computing, 2021, 2021. Link

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[C]. ICC 2021-2021 IEEE International Conference on Communications. IEEE, 2021. 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

• Shao Y, Li R, Hu B, et al. Graph Attention Network-based Multi-agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10792-10803. Link
• 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
• Dong T, Zhuang Z, Qi Q, et al. Intelligent Joint Network Slicing and Routing via GCN-powered Multi-Task Deep Reinforcement Learning[J]. IEEE Transactions on Cognitive Communications and Networking, 2021. Link
• 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

• Zhao J, Yang C. Graph Reinforcement Learning for Predictive Power Allocation to Mobile Users[J]. arXiv preprint arXiv:2203.03906, 2022. Link
• 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

• Huang R, Guan W, Zhai G, et al. Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks[J]. Applied Sciences, 2022, 12(4): 1951. Link
• 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

• Fang Y, Ergüt S, Patras P. SDGNet: A Handover-Aware Spatiotemporal Graph Neural Network for Mobile Traffic Forecasting[J]. IEEE Communications Letters, 2022, 26(3): 582-586. Link
• Zhao S, Jiang X, Jacobson G, et al. Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach[C]//2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2020: 1-9. Link
• 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

• He S, Yuan J, An Z, et al. Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems[J]. arXiv preprint arXiv:2112.01738, 2021. Link
• Chen T, You M, Zheng G, et al. Graph Neural Network Based Beamforming in D2D Wireless Networks[C]. The 25th International ITG Workshop on Smart Antennas (WSA), Nov. 2021, EURECOM, French Riviera. Link
• Zhang X, Zhao H, Xiong J, et al. Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks[C]//GLOBECOM 2021-2021 IEEE Global Communications Conference. IEEE, 2021. Link

Beam Selection and Link Activation

• He S, Xiong S, Zhang W, et al. GBLinks: GNN-based beam selection and link activation for ultra-dense D2D mmWave networks[J]. IEEE Transactions on Communications, 2022. Link

Wireless Link Scheduling

• Zhao Z, Verma G, Swami A, et al. Delay-Oriented Distributed Scheduling Using Graph Neural Networks[C]. ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 8902-8906. Link Code
• Zhao Z, Verma G, Rao C, et al. Distributed scheduling using graph neural networks[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 4720-4724. Link
• Sambamoorthy R, Mandalapu J, Peruru S S, et al. Graph Neural Network Based Scheduling: Improved Throughput Under a Generalized Interference Model[C]//EAI International Conference on Performance Evaluation Methodologies and Tools. Springer, Cham, 2021: 144-153. Link
• Zhao Z, Verma G, Rao C, et al. Link scheduling using graph neural networks[J]. arXiv preprint arXiv:2109.05536, 2021. Link Code
• Naderializadeh N. Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels[J]. arXiv preprint arXiv:2110.01722, 2021. Link Code
• Li P, Wang L, Wu W, et al. Graph Neural Network-Based Scheduling for Multi-UAV-Enabled Communications in D2D Networks[J]. Digital Communications and Networks, 2022. Link
• 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

Others

• Yanyan Z, Zeyu L, Baocong W. Graph convolution network deep reinforcement learning approach based on manifold regularization in cognitive radio network[C]//2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021: 1275-1280. Link

IoT Network

Anomaly Detection

• Wu Y, Dai H N, Tang H. Graph neural networks for anomaly detection in industrial internet of things[J]. IEEE Internet of Things Journal, 2021. Link

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

• Zhou X, Liang W, Li W, et al. Hierarchical adversarial attacks against graph neural network based IoT network intrusion detection system[J]. IEEE Internet of Things Journal, 2022. Link
• Chang L, Branco P. Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms[J]. arXiv preprint arXiv:2111.13597, 2021. Link
• Lo W W, Layeghy S, Sarhan M, et al. E-GraphSAGE: A Graph Neural Network based Intrusion Detection System[C]. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2022: 1-9. Link

Resource Allocation

• Chen T, Zhang X, You M, et al. A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks[J]. IEEE Internet of Things Journal, 2021, 9(3): 1712-1724. 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

Others

• Chen G, Wu J, Yang W, et al. Leveraging Graph Convolutional-LSTM for Energy Efficient Caching in Blockchain-based Green IoT[J]. IEEE Transactions on Green Communications and Networking, 2021. Link
• Tang M, Cai S, Lau V K N. Over-the-Air-Aggregation with Multiple Shared Channels and Graph-based State Estimation for Industrial IoT Systems[J]. IEEE Internet of Things Journal, 2021. Link

Satellite Network

Routing

• Liu M, Li J, Lu H. Routing in Small Satellite Networks: A GNN-based Learning Approach[J]. arXiv preprint arXiv:2108.08523, 2021. Link
• Wang H, Ran Y, Zhao L, et al. GRouting: Dynamic Routing for LEO Satellite Networks with Graph-based Deep Reinforcement Learning[C]//2021 4th International Conference on Hot Information-Centric Networking (HotICN). IEEE, 2021: 123-128. Link

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

Others

• Cao H, Zhu W, Feng W, et al. Robust beamforming based on graph attention networks for IRS-assisted Satellite IoT Communications[J]. Entropy, 2022, 24(3): 326. 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[C]. ICC 2021-2021 IEEE International Conference on Communications (ICC). IEEE, 2021. Link

Load Forecasting

• Zheng H, Ding X, Wang Y, et al. Attention Based Spatial-Temporal Graph Convolutional Networks for RSU Communication Load Forecasting[C]//International Conference on Collaborative Computing: Networking, Applications and Worksharing. Springer, Cham, 2021: 99-114. 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 Code

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

BGP Anomaly Detection

• Peng S, Nie J, Shu X, et al. A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network[J]. arXiv preprint arXiv:2112.12793, 2021. Link

Botnet Detection

• Yang Y, Wang L. LGANet: Local Graph Attention Network for Peer-to-Peer Botnet Detection[C]//2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC). IEEE, 2021: 31-36. Link
• 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

Encrypted Traffic Classification

• Huoh T L, Luo Y, Zhang T. Encrypted Network Traffic Classification Using a Geometric Learning Model[C]//2021 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2021: 376-383. Link
• Mo S, Wang Y, Xiao D, et al. Encrypted Traffic Classification Using Graph Convolutional Networks[C]//International Conference on Advanced Data Mining and Applications. Springer, Cham, 2020: 207-219. Link
• Pang B, Fu Y, Ren S, et al. CGNN: Traffic Classification with Graph Neural Network[J]. arXiv preprint arXiv:2110.09726, 2021. Link
• Busch J, Kocheturov A, Tresp V, et al. NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification[C]. 33rd International Conference on Scientific and Statistical Database Management (SSDBM 2021), 2021. Link
• Pham T D, Ho T L, Truong-Huu T, et al. MAppGraph: Mobile-App Classification on Encrypted Network Traffic using Deep Graph Convolution Neural Networks[C]//Annual Computer Security Applications Conference. 2021: 1025-1038. Link Code and Data
• Sun B, Yang W, Yan M, et al. An encrypted traffic classification method combining graph convolutional network and autoencoder[C]//2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC). IEEE, 2020: 1-8. 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
• Hu Z, Liu L, Yu H, et al. Using Graph Representation in Host-Based Intrusion Detection[J]. Security and Communication Networks, 2021, 2021. Link
• 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, Scheffler A, Bondorf S. Network Calculus with Flow Prolongation--A Feedforward FIFO Analysis enabled by ML[J]. arXiv preprint arXiv:2202.03004, 2022. Link
• 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

• Wang M, Hui L, Cui Y, et al. xNet: Improving Expressiveness and Granularity for Network Modeling with Graph Neural Networks[C]//IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2022: 2028-2037. Link
• Happ M, Du J L, Herlich M, et al. Exploring the Limitations of Current Graph Neural Networks for Network Modeling[C]. Proceedings of the IEEE/IFIP Network Operations and Management Symposium, 2022. Link
• 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

Performance Prediction

• Soto P, Camelo M, Mets K, et al. ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs[J]. Sensors, 2021, 21(13): 4321. Link Data
• Kong Y, Petrov D, Räisänen V, et al. Path-Link Graph Neural Network for IP Network Performance Prediction[C]//2021 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2021: 170-177. Link

Routing

• Yan B, Liu Q, Shen J L, et al. Flowlet-level multipath routing based on graph neural network in OpenFlow-based SDN[J]. Future Generation Computer Systems, 2022, 134: 140-153. Link
• Mai X, Fu Q, Chen Y. Packet Routing with Graph Attention Multi-agent Reinforcement Learning[J]. arXiv preprint arXiv:2107.13181, 2021. Link
• Almasan P, Xiao S, Cheng X, et al. ENERO: Efficient Real-Time Routing Optimization[J]. arXiv preprint arXiv:2109.10883, 2021. Link
• Güemes-Palau C, Almasan P, Xiao S, et al. Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies[C]//NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2022: 1-5. Link
• Chen B, Zhu D, Wang Y, et al. An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization[J]. Electronics, 2022, 11(3): 368. Link
• 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

• Qin L, Wei W, Ma Y. FlowDiviner: Spatio-Temporal Network Traffic Prediction Method Based on Graph Neural Network[C]. 5th Asia-Pacific Workshop on Networking (APNet 2021). Link
• Yao Z, Xu Q, Chen Y, et al. Internet Traffic Forecasting using Temporal-Topological Graph Convolutional Networks[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021: 1-8. Link
• 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

• Shabka Z, Zervas G. Nara: Learning Network-Aware Resource Allocation Algorithms for Cloud Data Centres[J]. arXiv preprint arXiv:2106.02412, 2021. Link
• Zhang K, Xu X, Fu C, et al. Modeling Data Center Networks with Message Passing Neural Network and Multi-task Learning[C]//International Conference on Neural Computing for Advanced Applications. Springer, Singapore, 2021: 96-112. Link
• 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

• Vinchoff C, Chung N, Gordon T, et al. Traffic Prediction in Optical Networks Using Graph Convolutional Generative Adversarial Networks[C]//2020 22nd International Conference on Transparent Optical Networks (ICTON). IEEE, 2020: 1-4. Link
• Bao B, Yang H, Wan Y, et al. Node-Oriented Traffic Prediction and Scheduling Based on Graph Convolutional Network in Metro Optical Networks[C]//Optical Fiber Communication Conference. Optical Society of America, 2021: F2G. 2. Link
• 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

Others

• Li B, Zhu Z. GNN-based Hierarchical Deep Reinforcement Learning for NFV-Oriented Online Resource Orchestration in Elastic Optical DCIs[J]. Journal of Lightwave Technology, 2021. Link
• Wang C, Yoshikane N, Tsuritani T. Usage of a Graph Neural Network for Large-Scale Network Performance Evaluation[C]//2021 International Conference on Optical Network Design and Modeling (ONDM). IEEE, 2021: 1-5. Link
• Tian X, Li B, Gu R, et al. Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning[J]. Journal of Optical Communications and Networking, 2021, 13(11): 253-265. Link

Software Defined Networking Scenario

Attack Detection

• Cao Y, Jiang H, Deng Y, et al. Detecting and Mitigating DDoS Attacks in SDN Using Spatial-Temporal Graph Convolutional Network[J]. IEEE Transactions on Dependable and Secure Computing, 2021. Link

Network Modeling

• Ferriol-Galmés M, Rusek K, Suárez-Varela J, et al. Routenet-erlang: A graph neural network for network performance evaluation[C]//IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2022: 2018-2027. Link Code
• 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

• Swaminathan A, Chaba M, Sharma D K, et al. GraphNET: Graph Neural Networks for routing optimization in Software Defined Networks[J]. Computer Communications, 2021, 178: 169-182. Link
• Khan T A, Abbas K, Rivera J J D, et al. Applying RouteNet and LSTM to Achieve Network Automation: An Intent-based Networking Approach[C]//2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2021: 254-257. Link
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Link State Prediction

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