基于多通道集多项式的极端数据稀缺标签正则化图神经网络

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jingxiao Zhang , Shifei Ding , Jian Zhang , Lili Guo , Ling Ding
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引用次数: 0

摘要

图神经网络(Graph Neural Networks, gnn)是一种常用的半监督节点分类方法。其优点在于对数据中的关系信息进行建模,并将标记节点的特征信息传播到图中未标记的节点,从而预测其标签。然而,目前的研究结果表明,当标记数据非常有限时,现有的模型表现不佳。为了解决这一问题,我们引入了一种标记正则化方法,并提出了一种基于多通道集多项式的抗极端数据稀缺(MSP-LR)标记正则化图神经网络。它由两个部分组成:一个基于多通道集合多项式的基本学习模块和一个标签正则化模块。具体来说,我们使用基本模块扩展模型的接受域并获得所有节点的伪标签。对于标记节点,我们将获得的伪标签信息替换为其初始标签信息。在标签正则化模块中,我们基于聚类假设对未标记节点施加正则化约束,以提高标签的可靠性。在不同标注率的2个同质图和4个异质图上的实验结果证明了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-channel set polynomial based label regularized graph neural networks against extreme data scarcity
Graph Neural Networks (GNNs) are one of the commonly used methods for semi-supervised node classification. Their advantage lies in modeling the relational information in the data and propagating the feature information of labeled nodes to unlabeled nodes in the graph, thereby predicting their labels. However, current research results indicate that existing models perform poorly when labeled data are extremely limited. To address this problem, we introduce a label regularization method and propose a multi-channel set polynomial based label regularized graph neural network against extreme data scarcity (MSP-LR). It consists of two components: a basic learning module based on multi-channel set polynomials and a label regularization module. Specifically, we use the basic module to expand the model's receptive field and obtain pseudo-labels for all nodes. For labeled nodes, we replace the obtained pseudo-label information with their initial label information. In the label regularization module, we impose regularization constraints on unlabeled nodes based on the clustering assumption to improve the reliability of labels. Experimental results on two homogeneous graphs and four heterogeneous graphs with different labeling rates demonstrate the effectiveness of this model.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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