通过基于课程的培训来增强签名图形神经网络
Zeyu Zhang1, Lu Li1, Xingyu Ji1
1the College of the Informatics, Huazhong Agricultural University, China.
概括
这项研究引入了Signed Graph神经网络 (SGNN) 的新课程学习框架,通过按难度排序的边缘训练来提高模型的准确性和稳定性. CSG框架提高了SGNN在现实世界签名图表数据上的性能.
科学领域:
- 图形理论
- 机器学习
- 网络科学
背景情况:
- 签名图表可以模拟出正负关系的复杂关系.
- 签名图神经网络 (SGNN) 是分析签名图的新兴工具.
- 目前的SGNN培训缺乏结构化的方法,使用随机抽样排序.
研究的目的:
- 为SGNN制定一个专门的培训计划.
- 解决签名图表中不同边缘学习困难的挑战.
- 提高SGNN模型的性能和稳定性.
主要方法:
- 提出了一个课程表现学习框架,用于签名图 (CSG).
- 开发了一种轻量级的难度测量器,用于签名图的边缘.
- 实施了一个调度器来订购SGNN的训练样本,从容易到困难.
主要成果:
- 提高了SGNN模型的准确度,达到23.7%.
- 减少了8.4%的标准偏差,改善了模型的稳定性.
- 在六个实体图表数据集上经验验证.
结论:
- 通过优化培训样本顺序, 课程学习对SGNN有显著的好处.
- CSG框架提供了一种实用且有效的SGNN培训方法.
- 提出的方法导致更准确,更稳定的符号图表表示学习.
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