The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index.
Graph-based deep learning for communication networks: A survey
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
(2016) · Cited by 11,679
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890
Cited by
Graph neural network for traffic forecasting: A survey
Jiang, W., & Luo, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117921 · Cited by 1,316
Self-citationHe, Y., Deng, Z., Chen, J., Li, W., Zhou, J., Xiang, F., & Hu, X. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.06.024 · Cited by 75
GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT
Altaf, T., Wang, X., Ni, W., Yu, G., Liu, R. P., & Braun, R. (2024) · Electronics · doi:10.3390/electronics13122274 · Cited by 50
BS-GAT: a network intrusion detection system based on graph neural network for edge computing
Wang, Y., Han, Z., Du, Y., Li, J., & He, X. (2025) · Cybersecurity · doi:10.1186/s42400-024-00296-8 · Cited by 47
When game theory meets satellite communication networks: A survey
Jiang, W., Han, H., He, M., & Gu, W. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.02.005 · Cited by 36
Self-citation
Metadata
- Authors
- Jiang, W.
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 185
- Pages
- 40-54
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W3172408309
- Cited by
- 284
- References
- 145
- FWCI
- 16.41
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Graph-based deep learning for communication networks: A survey. https://app.cochat.ai/papers/10.1016/j.comcom.2021.12.015
- The paper
- Jiang, W. (2022). Graph-based deep learning for communication networks: A survey. Computer Communications. https://doi.org/10.1016/j.comcom.2021.12.015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
