在图形神经网络的数据增强中利用集体结构知识
Rongrong Ma1, Guansong Pang2, Ling Chen1
1Faculty of Engineering and Information Technology, University of Technology Sydney, 123 Broadway, Sydney, 2007, NSW, Australia.
概括
本研究介绍了一种集体结构知识增强图形神经网络 (CoS-GNN),以增强图形表示学习. CoS-GNN有效地结合了各种结构特征,显著提高了图形分类和异常检测任务的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 擅长通过消息传递学习图形表示.
- 当前的GNN经常忽视关键的节点和图形结构信息,限制了它们的表达力.
- 现有的图形数据增强方法在多个结构特征下难以扩展.
研究的目的:
- 为了提出一种新的方法,集体结构知识增强图形神经网络 (CoS-GNN).
- 为了使GNN能够利用各种各样的节点和图层结构特征.
- 改进GNN中结构知识的建模,以实现增强的图形表示.
主要方法:
- 在COS-GNN中引入了一种新的消息传递方法.
- 集成多种节点和图层结构特征与原始节点属性.
- 增强图形以纳入集体结构知识.
主要成果:
- 在节点和图表层面上,CoS-GNN显著增强了结构知识建模.
- 与现有方法相比,实现了大幅度改进的图形表示.
- 在图形分类,异常检测和分布外概括方面表现优于最先进的模型.
结论:
- 通过整合集体结构知识,CoS-GNN有效地解决了传统GNN的局限性.
- 拟议的方法为高级图形表示学习提供了可扩展和强大的方法.
- 在各种图表级学习任务中表现出卓越的表现.
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