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    • Multidimensional graph contrastive learning based on graph enhancement and multi-neural networks

    • In the field of unsupervised graph representation learning, researchers have proposed the LAST-MGCL model, which effectively improves the quality of node representations through local global graph enhancement techniques and multi neural network collaborative modeling, providing an effective solution for unsupervised graph representation learning.
    • Vol. 30, Issue 9, Pages: 3097-3110(2025)   

      Received:07 November 2024

      Revised:2025-04-08

      Accepted:09 April 2025

      Published:16 September 2025

    • DOI: 10.11834/jig.240612     

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  • Jiang Xuchu, Zhang Xiaowen. 2025. Multidimensional graph contrastive learning based on graph enhancement and multi-neural networks. Journal of Image and Graphics, 30(9):3097-3110 DOI: 10.11834/jig.240612.
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相关作者

Jiang Xuchu 中南财经政法大学统计与数学学院
Zhang Xiaowen 中国人民大学统计学院
Jin Lianwen 华南理工大学电子与信息学院;华南理工大学—珠海现代产业创新研究院
Zhang Shuo 华中科技大学人工智能与自动化学院
Huang Mingxin 华南理工大学电子与信息学院
Liao Wenhui 华南理工大学电子与信息学院
Liu Yuliang 华中科技大学人工智能与自动化学院
Li Hongliang 华南理工大学电子与信息学院

相关机构

School of Electronic and Information Engineering, South China University of Technology
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology
SCUT-Zhuhai Institute of Modern Industrial Innovation
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University
Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing
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