HGATE: Heterogeneous Graph Attention Auto-Encoders

Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, complex relationships in various entities can be represented by heterogeneous gr...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 4; S. 3938 - 3951
Hauptverfasser:	Wang, Wei, Suo, Xiaoyang, Wei, Xiangyu, Wang, Bin, Wang, Hao, Dai, Hong-Ning, Zhang, Xiangliang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coders Decoding Encoders-Decoders Graph embedding representation Graph neural networks Graph representations Graph theory Graphical representations heterogeneous graphs hierarchical attention inductive learning Labeling Nodes Representation learning Semantics Structured data Task analysis transductive learning Unsupervised learning
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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