电子科技大学学报2025，Vol.54Issue(3)：455-463,9.DOI:10.12178/1001-0548.2024084
基于集成学习的不平衡图节点分类算法
Unbalanced graph node classification algorithm based on ensemble learning
摘要
Abstract
Graph neural network(GNNs)has been widely employed in node classification over the past few years.However,existing research has predominantly focused on balanced datasets,whereas imbalanced data is prevalent.Traditional approaches to handling imbalanced datasets,such as resampling and reweighting,often require substantial preprocessing or proposing new network structures,which can introduce new biases and lead to information loss.An enhanced bootstrap aggregating(Bagging)ensemble learning method is proposed to address imbalanced graph datasets.It involves partitioning the data into k folds and training multiple distinct sub-models using GNNs as the base model.Finally,by fusing different models,the node classification accuracy is improved without introducing excessive preprocessing.Experimental results on imbalanced graph datasets demonstrate that the proposed method outperforms the base classifier in terms of accuracy and robustness.Additionally,it is observed that classification accuracy initially increases and then decreases with the increase of k.关键词
图神经网络/节点分类/图网络结构/不平衡图数据集/集成学习Key words
graph neural network/node classification/graph network structure/imbalanced graph data set/ensemble learning分类
信息技术与安全科学引用本文复制引用
赵华健,杨钦程,胡兆龙..基于集成学习的不平衡图节点分类算法[J].电子科技大学学报,2025,54(3):455-463,9.基金项目
国家自然科学基金(62103375) （62103375）
浙江省自然科学基金(LY23F030003) （LY23F030003）
