DC Field | Value | Language |
dc.contributor.author | Yanxiang Zhao | - |
dc.contributor.author | Yijun Zhou | - |
dc.contributor.author | Zhijie Han | - |
dc.coverage.spatial | Минск | en_US |
dc.date.accessioned | 2024-03-01T08:00:13Z | - |
dc.date.available | 2024-03-01T08:00:13Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Yanxiang Zhao. Graph Neural Networks for Communication Networks: A Survey / Yanxiang Zhao, Yijun Zhou, Zhijie Han // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 90–94. | en_US |
dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54458 | - |
dc.description.abstract | Communication networks are an important
infrastructure in contemporary society. In recent years, based
on the advancement and application of machine learning and
deep learning in communication networks, the most advanced
deep learning method, Graph Neural Network (GNN), has been
applied to understand multi-scale deep correlations, provide
generalization ability, and improve the accuracy indicators of
predictive modeling. In this survey, we reviewed various issues
using different graph based deep learning models in different
types of communication networks. Optimize control strategies,
including offloading strategies, routing optimization, resource
allocation, etc. Finally, we discussed potential research
challenges and future directions. | en_US |
dc.language.iso | en | en_US |
dc.publisher | BSU | en_US |
dc.subject | материалы конференций | en_US |
dc.subject | deep learning | en_US |
dc.subject | graph neural networks | en_US |
dc.subject | reinforcement learning | en_US |
dc.title | Graph Neural Networks for Communication Networks: A Survey | en_US |
dc.type | Article | en_US |
Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
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