标签解卷对节点表示学习大规模的归因图表对学习偏差的学习
IEEE transactions on pattern analysis and machine intelligence
|September 12, 2024
概括
标签解卷 (LD) 减少了对属性图的节点表示学习中的学习偏差. 这种高效的技术接近逆图神经网络 (GNN) 映射,改进了属性和结构编码.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 在属性图表上学习节点表示对于下游任务至关重要.
- 目前的方法将预先训练的模型 (节点编码器) 与GNN相结合.
- 在大型图表上联合训练大型模型会带来可扩展性挑战,导致单独的训练和学习偏见.
研究的目的:
- 提出一个高效的标签规范化技术,标签解卷 (LD).
- 通过近似GNN的反向映射来减轻学习偏差.
- 将GNN特征卷曲纳入节点编码器培训.
主要方法:
- 开发了标签解卷 (LD),一种可扩展的标签规范化技术.
- 接近GNN的反向映射,以创建与联合培训相当的目标功能.
- 将GNN纳入节点编码器训练阶段,以减轻学习偏差.
主要成果:
- LD有效地将GNN纳入节点编码器训练中,减少学习偏差.
- 在温和假设下,LD汇聚到通过联合训练实现的最佳目标功能值.
- 对开放图标基准数据集的实验表明,LD显著超过了最先进的方法.
结论:
- 标签解卷 (LD) 提供了一种高效且可扩展的解决方案,用于在属性图上学习节点表示.
- 在单独训练的节点编码器和GNN中,LD成功地解决了学习偏差问题.
- 与基准数据集上的现有技术相比,拟议的方法显示出更高的性能.
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