Graph Attention Networks: A Comprehensive Review of Methods and Applications

被引:0
|
作者
Vrahatis, Aristidis G. [1 ]
Lazaros, Konstantinos [1 ]
Kotsiantis, Sotiris [2 ]
机构
[1] Ionian Univ, Dept Informat, Corfu 49100, Greece
[2] Univ Patras, Dept Math, Patras 49100, Greece
关键词
graph attention networks; graph neural networks; graph convolution networks; IMAGE SUPERRESOLUTION; ANOMALY DETECTION; TRAFFIC FLOW; CLASSIFICATION; PREDICTION; FRAMEWORK;
D O I
10.3390/fi16090318
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:34
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