Dual-decoder graph autoencoder for unsupervised graph representation learning

Unsupervised graph representation learning is a challenging task that embeds graph data into a low-dimensional space without label guidance. Recently, graph autoencoders have been proven to be an effective way to solve this problem in some attributed networks. However, most existing graph autoencode...

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Veröffentlicht in:	Knowledge-based systems Jg. 234; S. 107564
Hauptverfasser:	Sun, Dengdi, Li, Dashuang, Ding, Zhuanlian, Zhang, Xingyi, Tang, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 25.12.2021 Elsevier Science Ltd
Schlagworte:	Affinity Algorithms Attributes Clustering Decoders Embedding Feature maps Graph autoencoder Graph clustering Graph embedding Graph neural networks Graph representation learning Graph representations Graphical representations Learning Machine learning Representation Graph clustering Graph representation learning Graph autoencoder Graph neural networks Graph embedding
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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