计算机工程与应用2024，Vol.60Issue(17)：148-157,10.DOI:10.3778/j.issn.1002-8331.2306-0254
基于数据与特征增强的自监督图表示学习方法
Self-Supervised Graph Representation Learning Method Based on Data and Feature Augmentation
摘要
Abstract
Graph representation learning plays a crucial role in handling graph data structures,but it faces a significant challenge of heavy reliance on labeled information.To overcome this challenge,a novel self-supervised graph representa-tion learning framework is proposed.By leveraging contrastive learning methods,it integrates the structural and attribute information of the original graph,as well as the high-and low-frequency information in the spectral domain,enhancing the preserved node information.Additionally,residual fusion and unbiased feature augmentation are employed to ensure feature effectiveness while further reducing bias in augmented samples.Moreover,in the contrastive part,the probability of negating the samples as true is estimated,and weights are used to measure the hardness and similarity of negations.Exper-iments on three public datasets prove that the performance in the downstream tasks of node classification is not only better than the current state-of-the-art unsupervised methods but also surpasses previous supervised methods in most tasks.关键词
自监督学习/图对比学习/特征增强/节点分类/图表示学习Key words
self-supervised learning/graph contrastive learning/feature augmentation/node classification/graph repre-sentation learning分类
信息技术与安全科学引用本文复制引用
许云峰,范贺荀..基于数据与特征增强的自监督图表示学习方法[J].计算机工程与应用,2024,60(17):148-157,10.基金项目
河北省重点研发计划项目(21373802D) （21373802D）
教育部人工智能协同育人项目(201801003011). （201801003011）
