多视图子图神经网络:用稀缺的标记数据进行自我监督的学习.
IEEE transactions on neural networks and learning systems
|October 22, 2024
概括
本研究介绍了Muse,这是一种新的自主监督学习框架,使用多视图子图来提高图形神经网络在低数据场景中的性能. Muse有效地捕捉了本地结构和远程依赖关系,提高了节点分类的准确性.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 自主监督学习学习
背景情况:
- 图形神经网络 (GNN) 在节点分类方面表现出色,但需要大量的标记数据.
- 由于监督不足和过度装配,低数据制度阻碍了GNN的性能.
- 捕获远程依赖对于稳健的节点表示至关重要.
研究的目的:
- 开发一种新的自主监督学习 (SSL) 框架,以解决低数据场景中 GNNs 的局限性.
- 通过有效利用本地和远程图形依赖来增强节点表示.
- 为了提高GNN在图形数据上的分类性能,使用有限的标记样本.
主要方法:
- 引入了一种自主监督的学习框架,称为多视图子图神经网络 (Muse).
- 提出了一种基于信息理论的机制,用于从输入和潜伏空间中识别子图.
- 合并子图捕捉局部结构和远程依赖关系,以创建全面的节点表示.
主要成果:
- Muse有效地通过捕捉本地图形结构和远程依赖关系来增强节点表示.
- 该框架在低数据模式中的节点分类任务中表现得更好.
- 理论概括误差极限验证了多视图子图融合的有效性.
结论:
- Muse提供了一种有前途的方法,用于在数据稀缺的环境中增强GNN.
- 利用多视图子图显著提高了学习节点表示的表达力.
- 该框架为现实世界的节点分类挑战提供了强大的解决方案,具有有限的标记数据.
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