Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths

Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However, existing HGNNs encounter great difficulty in...

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Hauptverfasser:	Sirui Shen, Daobin Zhang, Shuchao Li, Pengcheng Dong, Qing Liu, Xiaoyu Li, Zequn Zhang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2023-03-01
Schriftenreihe:	Applied Sciences
Schlagworte:	graph neural network heterogeneous graph representation learning heterogeneous graph purification graph representation
Online-Zugang:	https://www.mdpi.com/2076-3417/13/6/3989
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