.

Xiao-Jun Yang1, Yang-Hui Wu2, Zhi-Hao Zhang3

  • 1Guangdong University of Technology, Guangzhou 510006, China; Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education of China, Guangdong University of Technology, Guangzhou 510006, China; Peng Cheng Laboratory, Shenzhen 518055, China.

概括

这项研究引入了一个新的多类型的知识对比学习推 (MVKC) 模型. MVKC通过提高知识感知和解决数据稀疏性来增强图形神经网络 (GNN),以提供更好的建议.

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