Graph neural networks: Historical backgrounds, present revolutions, and conventionalization for the future

Graph neural networks (GNNs) have become a powerful framework for analyzing structured data in the form of graphs, with applications spanning diverse fields such as social networks, biology, and recommender systems. This survey explores methodology, development, and advances in GNN architecture. We...

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Vydané v:	International journal of data science and analytics Ročník 20; číslo 6; s. 5237 - 5299
Hlavní autori:	Maghdid, Sozan S., Rashid, Tarik A., Askar, Shavan K.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.11.2025 Springer Nature B.V
Predmet:	Artificial Intelligence Business Information Systems Computational Biology/Bioinformatics Computer Science Data Mining and Knowledge Discovery Database Management Datasets Graph neural networks Recommender systems Review Social networks Structured data Graph neural networks Recurrent-based GNNs (RecGNNs) Graph autoencoders (GAEs) Convolutional GNNs (ConvGNNs) Spatial–temporal graph neural networks (STGNNs)
ISSN:	2364-415X, 2364-4168
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