Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

Deep learning’s performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior resea...

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Veröffentlicht in:	ACM computing surveys 2024-05, Vol.56 (5), p.1-42, Article 126
Hauptverfasser:	Chen, Zhiqian, Chen, Fanglan, Zhang, Lei, Ji, Taoran, Fu, Kaiqun, Zhao, Liang, Chen, Feng, Wu, Lingfei, Aggarwal, Charu, Lu, Chang-Tien
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation algorithms Computer science Computing methodologies Deep learning Graph algorithms Graph neural networks Machine learning Mathematics of computing Neural networks Spectra of graphs Spectral methods
Online-Zugang:	Volltext
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