Deep embedded clustering with distribution consistency preservation for attributed networks

•A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters jointly.•A consistency constraint is designed to maintain the consistency of the clusters.•The empirical study verifies the effectiveness of the...

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Vydané v:	Pattern recognition Ročník 139; s. 109469
Hlavní autori:	Zheng, Yimei, Jia, Caiyan, Yu, Jian, Li, Xuanya
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.07.2023
Predmet:	Autoencoder Cluster distribution consistency Deep embedded clustering Graph autoencoder Node representation learning Graph autoencoder Node representation learning Autoencoder Cluster distribution consistency Deep embedded clustering
ISSN:	0031-3203, 1873-5142
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