基于层次对比学习的半监督节点分类算法

doi:10.16451/j.cnki.issn1003-6059.202308004

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摘要大多数用于半监督节点分类的图对比学习方法需要通过繁琐的图数据增强方式得到两个视图,而且这些数据增强方式会不可避免地改变图语义信息,限制现有图对比学习方法的效率和适用性.为此,文中提出基于层次对比学习的半监督节点分类算法.算法无需进行图数据增强,而是将图神经网络不同层次的表示作为对比的视图进行学习,从而缓解繁琐的搜索以及语义的破坏.此外,设计一种半监督对比损失,有效利用少量的标记信息和大量的无标记信息以提供丰富的监督信号和改进节点的表示.最后,在四个基准数据集上对节点分类任务的实验验证文中算法的有效性.

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	李雅琪
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	梁吉业

关键词 ：半监督节点分类, 图对比学习, 图数据增强, 半监督对比损失, 图神经网络

Abstract：Most graph contrastive learning methods for semi-supervised node classification obtain two views by cumbersome data augmentation. Moreover, the above data augmentation inevitably changes the graph semantic information, limiting the efficiency and applicability of the existing graph contrastive learning methods. Therefore, a semi-supervised node classification algorithm based on hierarchical contrastive learning is proposed in this paper. In the proposed algorithm, graph data augmentation is unnecessary and the representations of different hierarchies of the graph neural network are learned as contrasted views to alleviate the tedious search and the semantic destruction. In addition, a semi-supervised contrastive loss is designed, and a small amount of labeled information and a large amount of unlabeled information are effectively utilized to provide rich supervised signals and improve the node representations. Finally, node classification experiments on four benchmark datasets validate the effectiveness of the proposed algorithm.

Key words： Semi-Supervised Node Classification Graph Contrastive Learning Graph Data Augmentation Semi-Supervised Contrastive Loss Graph Neural Network

收稿日期: 2023-06-25

ZTFLH:

TP391

基金资助:国家自然科学基金项目(No.62276158,U21A20473)

通讯作者: 梁吉业,博士,教授.主要研究方向为数据挖掘、机器学习、大数据分析、人工智能.E-mail:ljy@sxu.edu.cn.

作者简介: 李雅琪,硕士研究生,主要研究方向为数据挖掘、机器学习.E-mail:liyaqi0126@163.com. 王杰,博士,讲师,主要研究方向为数据挖掘、机器学习.E-mail:wangjie@tyust.edu.cn. 王锋,博士,副教授.主要研究方向为特征选择、粒计算、机器学习.E-mail:sxuwangfeng@126.com.

引用本文:

李雅琪, 王杰, 王锋, 梁吉业. 基于层次对比学习的半监督节点分类算法[J]. 模式识别与人工智能, 2023, 36(8): 712-720. LI Yaqi, WANG Jie, WANG Feng, LIANG Jiye. Semi-Supervised Node Classification Algorithm Based on Hierarchical Contrastive Learning. Pattern Recognition and Artificial Intelligence, 2023, 36(8): 712-720.

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