Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization

Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists...

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Vydané v:	Entropy (Basel, Switzerland) Ročník 22; číslo 2; s. 197
Hlavní autori:	Choong, Jun Jin, Liu, Xin, Murata, Tsuyoshi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	MDPI 01.02.2020 MDPI AG
Predmet:	graph neural network network embedding variational autoencoder variational inference
ISSN:	1099-4300, 1099-4300
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