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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Bibliographic Details
Published in:	International journal of data science and analytics Vol. 20; no. 6; pp. 5237 - 5299
Main Authors:	Maghdid, Sozan S., Rashid, Tarik A., Askar, Shavan K.
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.11.2025 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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