图表对比学习与节点级准确差异
Pengfei Jiao1,2, Kaiyan Yu1, Qing Bao1
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China.
Fundamental research
|April 17, 2025
概括
精确的基于差异的节点级图形对比学习 (DNGCL) 量化图形差异以区分类似的图形. 这种新的方法通过关注节点级别的差异来改善自我监督学习,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 图形对比学习 (GCL) 在图形的自我监督学习中表现出色.
- 目前的GCL方法使用预定义的增量,可能会改变图形语义.
- 这可能会阻碍区分结构上相似但语义上不同的图形.
研究的目的:
- 开发一个GCL框架,准确量化图形差异.
- 增强模型区分有微妙差异的图形的能力.
- 为了改善图表样本之间的关系的捕获.
主要方法:
- 提出精确的基于差异的节点级图谱对比学习 (DNGCL).
- 训练一个节点区分器来区分原始和增强节点.
- 采用等号不相似度来测量节点级差异.
- 使用多个数据增强策略,以获得更丰富的本地信息.
主要成果:
- DNGCL有效地区分具有微小差异的相似图形.
- 该框架在六个基准数据集中展示了卓越的性能.
- 在图形对比学习任务中表现优于最先进的基线方法.
结论:
- 量化图形差异对于准确的GCL至关重要.
- DNGCL提供了一种强大的方法来学习节点级差异.
- 拟议的方法推进了自我监督的图形表示学习.
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