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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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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Summary: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 methodically survey the major classes of GNNs, including convolutional GNNs (ConvGNNs), spatial–temporal graph neural networks (STGNNs), recurrent-based GNNs (RecGNNs), and graph autoencoders (GAEs). Every model is discussed in terms of underlying mathematical formulations, design principles, and practical applications. This survey aims to provide a comprehensive understanding of GNNs for practitioners, students, and researchers alike, highlighting their versatility and potential for future innovations in graph neural networks. This review is broad, addressing the basic ideas behind GNNs, different architectural designs, training and inference methods, common issues and constraints, the variety of datasets used, and real-world applications across numerous fields. We will furthermore discuss applications of graph neural networks across different fields and exemplify open-source codes, benchmark datasets, and model valuation for graph neural networks. In the end, this survey specifies existing challenges in interpretability, generalization, and scalability and proposes possible future research trends to further promote the performance of GNNs across various graph-based learning missions.
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ISSN:2364-415X
2364-4168
DOI:10.1007/s41060-025-00797-w