阿里尔:对抗式图形对比式学习
Shengyu Feng1, Baoyu Jing2, Yada Zhu3
1Carnegie Mellon University, USA.
ACM transactions on knowledge discovery from data
|April 23, 2025
概括
逆向图形对比学习 (ArieL) 引入了对数据增强的逆向视图,改善了无监督图形表示学习. 这种方法产生了高质量的对比样本,在节点和图形分类任务中表现优于现有技术.
科学领域:
- 图形表示学习学习学习图形表示学习
- 没有监督的学习学习.
- 机器学习 机器学习
背景情况:
- 对比式学习是无监督图表表示学习的关键,依赖于正负样本构造.
- 现有的方法经常使用节点接近,而从计算机视觉中增强数据显示出有希望但对图形来说具有挑战性.
- 为图形数据增强生成高质量的对比样本仍然是一个可以改进的开放领域.
研究的目的:
- 提出一种简单而有效的方法,即对抗图对比学习 (ArieL),用于在图形表示学习中提取信息对比样本.
- 通过引入对抗式图形视图来解决图形领域数据增强的挑战.
- 为节点级和图形级对比学习概括拟议的方法.
主要方法:
- 引入了对数据增强的对抗图形视图,以生成信息化的对比样本.
- 开发了信息规范化,以提供稳定的培训,并为可扩展性进行子图采样.
- 通过将图形视为超级节点,将节点级别的方法推广到图形级别的对比学习.
主要成果:
- ArieL在现实数据集上的当前图形对比学习方法在节点级和图形级分类方面始终表现出色.
- 证明了ArieL对抗敌对攻击的增强强性.
- 在合理的限制范围内成功提取了高质量的对比样本.
结论:
- 逆向图形对比学习 (ArieL) 为无监督图形表示学习提供了一种强大的新方法.
- 该方法有效地解决了对比学习图形数据增强的局限性.
- 阿里尔显示了改善图形分类任务和稳定性的巨大潜力.
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