基于对抗数据增强的非平衡节点分类算法OA北大核心CSTPCD

Graph Neural Networks(GNNs)have achieved notable success in node classification tasks.However,current GNN models tend to focus on majority classes with a large amount of labeled data,paying little attention to minority classes with fewer labels.Traditional methods often address this issue through oversampling,which may lead to overfitting.Some recent studies suggest synthesizing additional nodes for minority classes from labeled nodes,yet there's no clear guarantee that these generated nodes truly represent the corresponding minority classes.In fact,incorrect synthetic nodes may undermine the generalization ability of the algorithm.To address this issue,this paper introduces a simple,self-supervised data augmentation method based on adversarial training,Graph A 2,which enhances the data by adding perturbations at the farthest gradient space around minority classes while using contrastive learning to ensure consistency after augmentation.This approach not only increases the diversity of data but also ensures smoothness and coherence across the entire space,thereby enhancing generalization capability.The experiments show that this method outperforms the current state-of-the-art baseline models on various imbalanced datasets.