GSSCL:基于集群标签平滑的图表自主监督课程学习的框架
Yang-Geng Fu1, Xinlong Chen1, Shuling Xu1
1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
概括
使用集群标签的图形自主监督学习 (GSSL) 方法可能会过度. 一个新的框架,GSSCL,使用课程学习和光滑的集群标签来提高模型的概括性和图形数据的性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 人工智能的人工智能
背景情况:
- 图形自主监督学习 (GSSL) 利用借口任务来进行未标记的图形数据.
- 现有的GSSL方法经常使用集群标签,这可能引入噪音并导致过度装配.
- 这种噪音可以降低模型的性能和通用性.
研究的目的:
- 提出一个新的框架,图表自主监督课程学习 (GSSCL),以解决现有的GSSL方法的局限性.
- 通过采用聚类标签光滑来提高通用性并减少GSSL中的过度拟合.
- 在图形学习中提高自我监督信号的可靠性.
主要方法:
- GSSCL采用课程学习策略,从容易到困难对集群进行排序.
- 它使用轮系数来评估节点集群的信心得分.
- 伪标签平滑应用于基于特征相似性的K-近邻图,以处理图形异构和杂链接.
主要成果:
- 拟议的GSSCL框架在各种图表基准中显示出卓越的性能.
- 它的结果与半监督节点分类中最先进的方法相美.
- 该框架在图形集群任务中表现出强的表现.
结论:
- GSSCL有效地减少了对精确集群的依赖,提高了模型的通用性.
- 该方法成功地减轻了GSSL中杂的集群标签产生的问题.
- GSSCL提供了一种强大的方法来从未标记的图形数据中学习,特别是在复杂或异构的图形结构中.
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