与对立的交叉视图重建和信息瓶的对比图表表示学习
Yuntao Shou1, Haozhi Lan1, Xiangyong Cao1
1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China.
概括
本研究介绍了对比图表表示学习 (CGRL),以解决图形神经网络 (GNN) 中的受欢迎偏见和噪音标签. 通过自适应地掩盖图形结构和使用信息瓶理论来实现强大的表示,CGRL增强了节点分类.
科学领域:
- 图形神经网络 (GNN) 是一个神经网络.
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 在信息聚合方面,GNN很出色,但在图形数据集中的受欢迎偏见和杂标签方面却存在困难.
- 现有的图形对比学习 (GCL) 方法经常最大限度地提高相互信息,可能引入冗余信息.
- 由于这些挑战,节点分类任务受到次优节点表示的阻碍.
研究的目的:
- 提出一种有效的GCL方法,CGRL,用于节点分类,可以克服受欢迎偏见和噪音标签.
- 通过掩盖节点和边缘来适应地学习最佳的图形结构表示.
- 为了提高节点特征表示的稳定性和模型概括性.
主要方法:
- 开发了对比图表表示学习 (CGRL),结合了对抗性交叉视图重建和信息瓶理论.
- 实现了适应性节点和边缘掩盖,以实现最佳的图形结构表示.
- 介绍了信息瓶理论,以过冗余信息和噪声干扰,用于对抗性重建.
主要成果:
- 为了获得最佳的表示,CGRL可自适应地掩盖图形结构.
- 信息瓶理论有效地消除了冗余信息,同时保留了与分类相关的细节.
- 对抗性交叉视图重建增强了节点特征表示的稳定性.
- 理论分析证实了拟议机制的有效性.
结论:
- 在现实数据集上,CGRL显著优于现有的最先进的算法,用于节点分类.
- 拟议的方法有效地解决了GNN中的受欢迎偏见和噪音标签.
- 在图形表示学习中,CGRL提供了更好的稳定性和概括性能.
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