Graph Attention Networks: A Comprehensive Review of Methods and Applications

Aristidis G. Vrahatis, Konstantinos Lazaros and Sotiris Kotsiantis ()
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Aristidis G. Vrahatis: Department of Informatics, Ionian University, 49100 Corfu, Greece
Konstantinos Lazaros: Department of Informatics, Ionian University, 49100 Corfu, Greece
Sotiris Kotsiantis: Department of Mathematics, University of Patras, 49100 Patras, Greece

Future Internet, 2024, vol. 16, issue 9, 1-34

Abstract: Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring numerous extensions and applications in several areas. In this review, we present a thorough examination of GATs, covering both diverse approaches and a wide range of applications. We examine the principal GAT-based categories, including Global Attention Networks, Multi-Layer Architectures, graph-embedding techniques, Spatial Approaches, and Variational Models. Furthermore, we delve into the diverse applications of GATs in various systems such as recommendation systems, image analysis, medical domain, sentiment analysis, and anomaly detection. This review seeks to act as a navigational reference for researchers and practitioners aiming to emphasize the capabilities and prospects of GATs.

Keywords: graph attention networks; graph neural networks; graph convolution networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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