Graph Neural Networks: Architectures, Stability, and Transferability

Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutiona...

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Veröffentlicht in:	Proceedings of the IEEE Jg. 109; H. 5; S. 660 - 682
Hauptverfasser:	Ruiz, Luana, Gama, Fernando, Ribeiro, Alejandro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Communication networks Convergence Convolutional neural networks Data processing Deep learning Equivariance Filter banks graph filters Graph neural networks graph neural networks (GNNs) graph signal processing (GSP) graphon neural networks graphons Graphs Information filters Information processing Neural networks Permutations Probability distribution Recommender systems Signal processing Stability Stability analysis Training data transferability Wireless communications Wireless networks
ISSN:	0018-9219, 1558-2256
Online-Zugang:	Volltext
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