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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Vydané v:	Pattern recognition Ročník 121; s. 108215
Hlavní autori:	Wang, Jie, Liang, Jiye, Yao, Kaixuan, Liang, Jianqing, Wang, Dianhui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.01.2022
Predmet:	Graph convolutional autoencoders Graph filter Graph representation learning Graph representation learning Graph filter Graph convolutional autoencoders
ISSN:	0031-3203, 1873-5142
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