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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54458
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dc.contributor.authorYanxiang Zhao-
dc.contributor.authorYijun Zhou-
dc.contributor.authorZhijie Han-
dc.coverage.spatialМинскen_US
dc.date.accessioned2024-03-01T08:00:13Z-
dc.date.available2024-03-01T08:00:13Z-
dc.date.issued2023-
dc.identifier.citationYanxiang 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.urihttps://libeldoc.bsuir.by/handle/123456789/54458-
dc.description.abstractCommunication 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.isoenen_US
dc.publisherBSUen_US
dc.subjectматериалы конференцийen_US
dc.subjectdeep learningen_US
dc.subjectgraph neural networksen_US
dc.subjectreinforcement learningen_US
dc.titleGraph Neural Networks for Communication Networks: A Surveyen_US
dc.typeArticleen_US
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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