节点转移与图形对比学习用于类不平衡的节点分类
Yangding Li1, Xiangchao Zhao1, Yangyang Zeng1
1College of Information Science and Engineering, Hunan Normal University, Changsha, China; Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
概括
本研究引入了一种新的节点转移与图形对比学习 (NT-GCL) 框架,以解决图形表示学习中的类不平衡. NT-GCL有效地平衡了节点数量和特征空间,改善了图形神经网络中的少数类表示.
科学领域:
- 图形表示学习学习学习图形表示学习
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 阶级不平衡是学习图形表示的一个主要挑战.
- 现有的方法在节点数量和特征空间不平衡方面都存在困难.
- 少数阶级经常因多数阶级的统治而处于不利地位.
研究的目的:
- 为了引入一个新的框架,节点转移与图形对比学习 (NT-GCL).
- 为了增强少数阶级的图形神经网络 (GNN) 代表性.
- 在不平衡的数据集中平衡节点数量和特征空间分布.
主要方法:
- 一个节点转移算法重新分配错误分类的节点,以平衡数量和特征空间.
- 这样可以防止多数类压缩少数类特征空间.
- 自主监督的对比学习培养了没有标签的模型,减少了偏见.
主要成果:
- NT-GCL有效地平衡了节点数量和特征空间分布.
- 该框架阻止了少数阶级的特征空间压缩.
- 实验表明NT-GCL在类不平衡节点分类中的强有力的竞争力.
结论:
- NT-GCL为类不平衡的节点分类提供了一个强大的解决方案.
- 提出的方法显著提高了GNN在不平衡图形数据上的性能.
- 该框架推进了对不平衡数据集的图形表示学习领域.
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