An Efficient Symmetric Attention Graph Neural Network Model

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- An Efficient Symmetric Attention Graph Neural Network Model
- “In the field of graph structured data processing, researchers have proposed a robust graph neural network model GSDAT, which reduces computational and data complexity through symmetric attention mechanism and ReLU function, and improves the accuracy of node classification and link prediction.”
- Advanced Engineering Sciences Pages: 1-11(2025)
- 作者机构：
  
  1.四川大学电气工程学院，四川成都 610065
  2.西北民族大学电气工程学院，甘肃兰州 730030
- 作者简介：
- 基金信息：
- DOI：10.12454/j.jsuese.202400688
  CLC： TP391.9
- Received：29 August 2024，
  
  Published Online：24 November 2025，
- 稿件说明：
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.An Efficient Symmetric Attention Graph Neural Network Model[J].Advanced Engineering Sciences,

.An Efficient Symmetric Attention Graph Neural Network Model[J].Advanced Engineering Sciences,
.An Efficient Symmetric Attention Graph Neural Network Model[J].Advanced Engineering Sciences, DOI：10.12454/j.jsuese.202400688.XXXX,XX(XX):1‒11.袁军,周丽君,张俊然.一种高效的对称注意力图神经网络模型[J].工程科学与技术,

.An Efficient Symmetric Attention Graph Neural Network Model[J].Advanced Engineering Sciences, DOI：10.12454/j.jsuese.202400688.

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