mlsanityData to 7 Oct 2026

Graph-based Deep Learning for Communication Networks: A Survey

arXiv 2106.02533 ↗First posted 4 Jun 2021cs.NI cs.LGcited by 21

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Abstract (arXiv)

Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved the state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks involving both wired and wireless scenarios. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 57 of its 131 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

57 of 131 references.

References · 57

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 3; 2025: 7; 2026: 3.

21 papers.

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  1. 2021Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithmscited by 11
  2. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  3. 2022Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directionscited by 8
  4. 2024Faithful Interpretation for Graph Neural Networkscited by 3
  5. 2024Hybrid variable spiking graph neural networks for energy-efficient scientific machine learningcited by 3
  6. 2025Cellular Traffic Prediction via Deep State Space Models with Attention Mechanismcited by 3
  7. 2021GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Frameworkcited by 2
  8. 2023Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networkscited by 2
  9. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  10. 2025Graph Your Own Promptcited by 2
  11. 2025Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clusteringcited by 1
  12. 2025RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networkscited by 1
9 more
  1. 2026An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networkscited by 1
  2. 2022Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by MLcited by 0
  3. 2023BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detectioncited by 0
  4. 2023Data-driven Intra-Autonomous Systems Graph Generatorcited by 0
  5. 2025Trajectory Encoding Temporal Graph Networkscited by 0
  6. 2025Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networkscited by 0
  7. 2025Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learningcited by 0
  8. 2026iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Deliverycited by 0
  9. 2026From Simulation to Deep Learning: Survey on Network Performance Modeling Approachescited by 0