半监督图形结构通过标签和先前结构的双重强化学习
IEEE transactions on cybernetics
|July 10, 2024
概括
本研究介绍了一种新的图形结构学习 (GSL) 方法,通过利用数据标签和先前的图形结构来增强图形神经网络 (GNNs). 这种方法提炼了图形数据,改善了GNN在不完美的现实世界图形上的性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 数据挖掘 数据挖掘
背景情况:
- 图形神经网络 (GNN) 通过消息传递优秀地处理图形数据,但假设完美的图形结构.
- 现实世界的图表往往是杂或不完整的,限制了GNN的性能.
- 目前的半监督图形结构学习 (GSL) 方法不充分利用可用的标签和先前的结构信息.
研究的目的:
- 开发一种改进的GLS方法,充分利用数据标签和先前图形结构.
- 在处理不完美的图形数据时,提高GNN的稳定性和性能.
主要方法:
- 构建先前的标签受约束矩阵以根据标签一致性来改进图形结构.
- 使用光谱对比学习从先前的图形结构中提取全局性质.
- 通过与先前的空间结构进行对比的融合,整合本地空间信息.
主要成果:
- 拟议的GLS方法在七个基准数据集中显示出有效性.
- 实验结果证实,使用已学习的图形结构,GNN的性能得到了提高.
- 学习的图形结构被证明是理性的,可以从多个角度解释.
结论:
- 标签和先前结构的双重增强显著改善了 GNN 的 GSL.
- 该方法提供了一个强大的解决方案,用于将GNN应用于现实世界,不完美的图形数据.
- 开发的技术为图形结构优化提供了更全面的方法.
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