Wasserstein Adversarially Regularized Graph Autoencoder

•Application of Wasserstein distance under graph settings.•Using Wasserstein distance for node embedding regularization.•Applying weight clipping and gradient penalty approaches for Lipschitz continuity.•Comparison of regularization against KL divergence and adversarial methods.•Detailed experiment...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 541; p. 126235
Main Authors:	Liang, Huidong, Gao, Junbin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 07.07.2023
Subjects:	Adversarial training Graph neural networks Variational autoencoder Wasserstein distance Variational autoencoder Wasserstein distance Graph neural networks Adversarial training 00–01 99–00
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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