NodeHGAE: Node-oriented heterogeneous graph autoencoder

Heterogeneous graph autoencoder (HGAE), as an unsupervised learning approach, aims to encode nodes and edges of heterogeneous graphs into low-dimensional vector representations, and simultaneously reconstruct the original graph structure from node representations. Existing heterogeneous graph encode...

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Vydané v:	Information sciences Ročník 719; s. 122448
Hlavní autori:	Zhu, Xiangkai, Li, Chao, Yan, Yeyu, Zhao, Zhongying, Duan, Hua, Zeng, Qingtian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.11.2025
Predmet:	Graph neural network Heterogeneous graph autoencoder Heterogeneous graph representation learning Heterogeneous graph autoencoder Heterogeneous graph representation learning Graph neural network
ISSN:	0020-0255
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