HetGNN-SF: Self-supervised learning on heterogeneous graph neural network via semantic strength and feature similarity

Li, C; Liu, XM; Yan, YY; Zhao, ZY; Zeng, QT

Zhao, ZY (通讯作者),Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qianwangang Rd, Qingdao 266590, Shandong, Peoples R China.

APPLIED INTELLIGENCE, 2023; 53 (19): 21902

Abstract

Heterogeneous graph neural networks (HGNNs) can effectively model multiple node types and complex interactions in real networks and solve problems in ......

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