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Graph Attention Networks: A Comprehensive Review of Methods and Applications

Author

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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)

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.

Suggested Citation

  • Aristidis G. Vrahatis & Konstantinos Lazaros & Sotiris Kotsiantis, 2024. "Graph Attention Networks: A Comprehensive Review of Methods and Applications," Future Internet, MDPI, vol. 16(9), pages 1-34, September.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:9:p:318-:d:1470540
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    Cited by:

    1. Weiwei Jiang & Haoyu Han & Yang Zhang & Ji’an Wang & Miao He & Weixi Gu & Jianbin Mu & Xirong Cheng, 2024. "Graph Neural Networks for Routing Optimization: Challenges and Opportunities," Sustainability, MDPI, vol. 16(21), pages 1-34, October.

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