# Graph-based Deep Learning for Communication Networks: A Survey

arXiv: https://arxiv.org/abs/2106.02533 · HTML: https://mlsanity.com/paper/2106.02533/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 131 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 30 of 57)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Stability Properties of Graph Neural Networks](https://mlsanity.com/paper/1905.04497/) (2019)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Learning Graph Embedding with Adversarial Training Methods](https://mlsanity.com/paper/1901.01250/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Graph Neural Networks: Architectures, Stability and Transferability](https://mlsanity.com/paper/2008.01767/) (2020)
- [Convergence and Stability of Graph Convolutional Networks on Large Random Graphs](https://mlsanity.com/paper/2006.01868/) (2020)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)

## Built on by (most cited first, 21 of 21)

- [Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms](https://mlsanity.com/paper/2111.13597/) (2021)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions](https://mlsanity.com/paper/2212.04047/) (2022)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Faithful Interpretation for Graph Neural Networks](https://mlsanity.com/paper/2410.06950/) (2024)
- [Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning](https://mlsanity.com/paper/2412.09379/) (2024)
- [Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism](https://mlsanity.com/paper/2506.15688/) (2025)
- [GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework](https://mlsanity.com/paper/2112.10767/) (2021)
- [Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2310.09800/) (2023)
- [Graph Your Own Prompt](https://mlsanity.com/paper/2509.23373/) (2025)
- [RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks](https://mlsanity.com/paper/2511.22321/) (2025)
- [Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering](https://mlsanity.com/paper/2504.01605/) (2025)
- [An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networks](https://mlsanity.com/paper/2602.04068/) (2026)
- [Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML](https://mlsanity.com/paper/2202.03004/) (2022)
- [BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection](https://mlsanity.com/paper/2304.07226/) (2023)
- [Data-driven Intra-Autonomous Systems Graph Generator](https://mlsanity.com/paper/2308.05254/) (2023)
- [Trajectory Encoding Temporal Graph Networks](https://mlsanity.com/paper/2504.11386/) (2025)
- [Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks](https://mlsanity.com/paper/2506.12786/) (2025)
- [Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning](https://mlsanity.com/paper/2508.14859/) (2025)
- [iSatCR: Graph-Empowered Joint Onboard Computing and Routing for LEO Data Delivery](https://mlsanity.com/paper/2603.18539/) (2026)
- [From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches](https://mlsanity.com/paper/2603.28394/) (2026)

## 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.