GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders

Variational graph autoencoders (VGAEs) combine the best of graph convolutional networks (GCNs) and variational inference and have been used to address various tasks such as node classification or link prediction. However, the lack of explainability is a limiting factor when trustworthy decisions are...

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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 9; H. 1; S. 281 - 291
Hauptverfasser:	Rodrigo-Bonet, Esther, Deligiannis, Nikos
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Architecture Artificial neural networks Autoencoders Computational modeling Data models Deep learning Effectiveness Encoders-Decoders Explainable deep learning geometric deep learning Graph convolutional networks graph convolutional neural networks Graph neural networks Graph theory layer-wise relevance propagation Network analysis Normal distribution post-hoc explanations Predictive models variational autoencoders
ISSN:	2471-285X, 2471-285X
Online-Zugang:	Volltext
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