Interpretable and Efficient Heterogeneous Graph Convolutional Network

Yang, Y, Guan, Z, Li, Jianxin, Zhao, W, Cui, J and Wang, Q 2021, Interpretable and Efficient Heterogeneous Graph Convolutional Network, IEEE Transactions on Knowledge and Data Engineering, pp. 1-14, doi: 10.1109/TKDE.2021.3101356.

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Title Interpretable and Efficient Heterogeneous Graph Convolutional Network
Author(s) Yang, Y
Guan, Z
Li, JianxinORCID iD for Li, Jianxin orcid.org/0000-0002-9059-330X
Zhao, W
Cui, J
Wang, Q
Journal name IEEE Transactions on Knowledge and Data Engineering
Start page 1
End page 14
Total pages 14
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2021
ISSN 1041-4347
1558-2191
Keyword(s) Task analysis
Convolution
Computer architecture
Scalability
Time complexity
Notes Early Access Article
Language eng
DOI 10.1109/TKDE.2021.3101356
Indigenous content off
Field of Research 08 Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30154740

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Created: Tue, 24 Aug 2021, 09:57:50 EST

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