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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Veröffentlicht in:	Pattern recognition Jg. 139; S. 109469
Hauptverfasser:	Zheng, Yimei, Jia, Caiyan, Yu, Jian, Li, Xuanya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2023
Schlagworte:	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
Online-Zugang:	Volltext
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