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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Bibliographic Details

Published in:	Information sciences Vol. 719; p. 122448
Main Authors:	Zhu, Xiangkai, Li, Chao, Yan, Yeyu, Zhao, Zhongying, Duan, Hua, Zeng, Qingtian
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.11.2025
Subjects:	Graph neural network Heterogeneous graph autoencoder Heterogeneous graph representation learning Heterogeneous graph autoencoder Heterogeneous graph representation learning Graph neural network
ISSN:	0020-0255
Online Access:	Get full text
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