集成技术2026，Vol.15Issue(5)：112-134,23.DOI:10.12146/j.issn.2095-3135.20260514002
从分布失衡到质量缺陷:鲁棒图表示学习方法综述
From Distribution Imbalance to Quality Defects:A Survey of Robust Graph Representation Learning Methods
摘要
Abstract
Graphs,as an important non-Euclidean data structure,are capable of modeling various entities and their complex relationships in the real world through interactions between nodes and edges.They have been widely applied in numerous domains,including social network analysis,citation network mining,traffic flow modeling,and financial risk control.Graph representation learning,which is central to mining the value of graph data,aims to map nodes,edges,or subgraphs into low-dimensional,dense vectors while preserving the structural and attribute information within the graph,providing high-quality representations for downstream tasks such as node classification and link prediction.In recent years,although deep learning-based graph representation learning has achieved significant progress,its performance is highly dependent on the quality of the input data.However,complex graph data suffer from distribution imbalances and quality defects in real-world scenarios,such as class imbalance,topological imbalance,label sparsity,and noise interference.Currently,there is a lack of systematic reviews and summaries on robust graph representation learning methods.Therefore,this paper aims to organize recent robust graph representation learning methods that address the aforementioned challenges.We first introduce relevant background knowledge and provide formal definitions.Subsequently,we review robust graph representation learning methods targeting class imbalance,topological imbalance,label sparsity,and noise interference.Finally,we discuss the current challenges faced by robust graph representation methods and prospect future research directions.关键词
图表示学习/图神经网络/鲁棒性/不平衡图/噪声干扰Key words
graph representation learning/graph neural networks/robustness/imbalanced graphs/noise interference分类
信息技术与安全科学引用本文复制引用
刘根,李超,赵中英..从分布失衡到质量缺陷:鲁棒图表示学习方法综述[J].集成技术,2026,15(5):112-134,23.基金项目
国家自然科学基金项目(62472263) （62472263）
教育部人文社科基金项目(24YJAZH058) （24YJAZH058）
中国高等教育学会2024年度高等教育科学研究规划项目(24XX0101) This work is supported by National Natural Science Foundation of China(62472263),Humanities and Social Science Research Project of the Ministry of Education,China(24YJAZH058),2024 Higher Education Scientific Research Planning Project of China Association of Higher Education(24XX0101) （24XX0101）
