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基于自监督信息增强的图表示学习

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对图表示学习模型依赖具体任务进行特征保留以及节点表示的泛化性有限等问题,本文提出一种基于自监督信息增强的图表示学习模型(Self-Variational Graph Auto Encoder,Self-VGAE).Self-VGAE首先使用图卷积编码器和节点表示内积解码器构建变分图自编码器(Variational Graph Auto Encoder,VGAE),并对原始图进行特征提取和编码;然后,使用拓扑结构和节点属性生成自监督信息,在模型训练过程中约束节点表示的生成.在多个图分析任务中,Self-VGAE的实验表现均优于当前较为先进的基线模型,表明引入自监督信息能够增强对节点特征相似性和差异性的保留能力以及对拓扑结构的保持、推断能力,并且Self-VGAE具有较强的泛化能力.
Graph Representation Learning Enhanced by Self-supervised In-formation
Graph representation learning models rely on specific task to preserve features,and the generaliza-tion of node representations are limited.Aiming at the above problems,a graph representation learning model Self-Variational Graph Auto Encoder(Self-VGAE)enhanced by self-supervised information is proposed in this article.Firstly,graph convolutional encoder and node representation inner product decoder are used to construct a VGAE.The feature extraction and coding of the original graph are performed.Then,the topology and node attributes are used to generate self-supervised information,and the generation of node representa-tion is constrained during model training.In multiple graph analysis tasks,the experimental performance of Self-VGAE is better than the current more advanced baseline model,which shows that the introduction of self-supervised information can enhance the ability to retain the similarity and difference of node features and the ability to maintain and infer the topology.Furthermore,Self-VGAE has a stronger generalization ability.

self-supervised informationgraph representation learninggraph variational auto encodersgraph convolutional networkscontrastive loss

袁立宁、文竹、冯文刚、刘钊

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中国人民公安大学国家安全学院,北京 100038

广西警察学院信息技术学院,广西南宁 530028

中国人民公安大学研究生院,北京 100038

自监督信息 图表示学习 图变分自编码器 图卷积网络 对比损失

国家重点研发计划项目中央高校基本科研业务费专项资金项目广西法学会法学研究课题广西哲学社会科学研究课题

2023YFC33216042022JKF02002GFKT2023-C323FTQ005

2024

10.13656/j.cnki.gxkx.20240619.013

广西科学
广西科学院 广西壮族自治区科学技术协会

广西科学

CSTPCD北大核心
影响因子：0.516
ISSN：1005-9164
年,卷(期)：2024.31(2)
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