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Updated: Jun 10, 2025

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GTC:GNN-变压器对比学习用于自我监督的异质图表表示

Yundong Sun1, Dongjie Zhu2, Yansong Wang2

  • 1Department of Electronic Science and Technology, Harbin Institute of Technology, Harbin, 150001, China; School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, China.

Neural networks : the official journal of the International Neural Network Society
|October 12, 2024
PubMed
概括

本研究介绍了GTC,这是一个结合图形神经网络 (GNN) 和变压器的新框架,以克服过度平滑并实现自我监督的图形表示学习. 在图表任务中,GTC有效地整合了本地和全球信息,以实现卓越的性能.

关键词:
图形神经网络的神经网络图形变压器 图形变压器不同质的图形表示形式.过度平滑是一种过度平滑.自主监督的图形学习学习

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