集成技术2025，Vol.14Issue(4)：71-86,16.DOI:10.12146/j.issn.2095-3135.20241217001
生成式图自监督学习综述
A Survey of Generative Graph Self-Supervised Learning
摘要
Abstract
Generative graph self-supervised learning is designed to harness the inherent information within graphs by crafting predictive tasks or structure/feature reconstruction tasks to produce supervisory signals.This process results in the generation of new graphs that closely mimic the original graph data in both structure and features.Thus,it has shown superior performance in addressing challenges such as data scarcity or insufficient labeling,attracting widespread attention from researchers.Despite significant progress in this field,there is still a lack of systematic organization and summarization.To this end,this paper aims to make a comparative study on the research achievements of generative graph self-supervised learning in recent years.It first introduces relevant background knowledge and provides formal definitions.Subsequently,it sorts out graph autoencoders in generative graph self-supervised learning and categorizes and analyzes graph mask autoencoders.In addition,this paper compiles commonly used datasets and evaluation metrics.Finally,it discusses the current challenges faced by generative graph self-supervised learning and offers prospects for future research directions.关键词
图自监督学习/生成式方法/图神经网络/图表示学习Key words
graph self-supervised learning/generative method/graph neural networks/graph representation learning分类
信息技术与安全科学引用本文复制引用
朱喜珍,张齐齐,赵中英..生成式图自监督学习综述[J].集成技术,2025,14(4):71-86,16.基金项目
国家自然科学基金项目(62272263,62072288) （62272263,62072288）
山东省自然科学基金项目(ZR2024MF034,ZR2022MF268). This work is supported by National Natural Science Foundation of China(62272263,62072288),Natural Science Foundation of Shandong Province(ZR2024MF034,ZR2022MF268) （ZR2024MF034,ZR2022MF268）
