对比的消息传递强大的图形神经网络与稀疏的标签.
概括
本研究介绍了使用对比信息传递的图形神经网络 (GNN) 的新耐噪框架. 该方法增强了对图形的半监督学习,具有有限的标签和结构噪声.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 网络科学 网络科学
背景情况:
- 图形神经网络 (GNN) 在半监督学习方面表现出色,但在有限的标签和结构性噪音方面扎.
- 在GNN中传递的传统消息对干扰很敏感,在杂的图表上降低了性能.
- 在训练数据稀缺时,过度装备是GNN的一个重要问题.
研究的目的:
- 为GNN制定一个强大的框架,以应对稀疏标签和结构噪声的挑战.
- 在现实场景中提高GNN的分类准确性和弹性.
- 引入超越有限节点标签的新型监督信号.
主要方法:
- 提出了一个耐噪声框架,利用对比的信息传递.
- 引入了对比图概率,定义为连接节点对的边缘概率的乘积.
- 实现了两个展开的更新步骤:功能更新与边缘概率初始化以及对同型/异型视图的二进制边缘应用.
主要成果:
- 在具有稀疏标签的半监督节点分类任务中表现出卓越的性能.
- 在图形数据中实现了对结构性扰动的卓越稳定性.
- 相反的方法有效地减轻了过度拟合,并增强了概括性.
结论:
- 拟议的对比性消息传递框架在具有挑战性的条件下显著提高了GNN的性能.
- 该方法为开发更可靠的GNN用于现实世界的应用提供了一个有希望的方向.
- 作为监督,拓结构的有效整合提高了模型的稳定性和准确性.
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