Graph convolutional autoencoders with co-learning of graph structure and node attributes

•We propose a novel end-to-end graph autoencoders model for the attributed graph.•The proposed model can reconstruct both the graph structure and node attributes.•The graph encoder is a completely low-pass filter.•The graph decoder is a completely high-pass filter.•Show the effectiveness of the prop...

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Veröffentlicht in:	Pattern recognition Jg. 121; S. 108215
Hauptverfasser:	Wang, Jie, Liang, Jiye, Yao, Kaixuan, Liang, Jianqing, Wang, Dianhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2022
Schlagworte:	Graph convolutional autoencoders Graph filter Graph representation learning Graph representation learning Graph filter Graph convolutional autoencoders
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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