用大模型生成少数类节点，解决图数据噪声标签下的类别不平衡问题
When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
- 用大模型生成合成少数类节点，缓解类别不平衡
- 动态加权伪标签减少噪声比例，提升标签可信度
- 双阶段大模型增强防止伪标签引发分布偏移，适合真实图数据
类别不平衡的图节点分类是现实但研究不足的问题。现有方法多假设标签干净可靠，但真实图数据常含噪声标签。本文系统研究带噪声标签的类别不平衡图上的鲁棒节点分类问题。提出GraphALP框架，结合大语言模型（LLM）与伪标签技术：设计基于LLM的过采样方法生成标签准确的少数类节点，缓解类别不平衡；在平衡图基础上，采用动态加权伪标签法获取高置信度伪标签以降低噪声比例；此外，引入二次LLM引导的过采样机制，缓解伪标签带来的类别分布偏移。实验表明，GraphALP在含噪声标签的类别不平衡图上优于当前最优方法。
原文摘要 · Abstract (English)
Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume clean and reliable labels when processing class-imbalanced graphs. This assumption often violates the nature of real-world graphs, where labels frequently contain noise. Given this gap, this paper systematically investigates robust node classification for class-imbalanced graphs with noisy labels. We propose GraphALP, a novel Graph Augmentation framework based on Large language models (LLMs) and Pseudo-labeling techniques. Specifically, we design an LLM-based oversampling method to generate synthetic minority nodes, producing label-accurate minority nodes to alleviate class imbalance. Based on the class-balanced graphs, we develop a dynamically weighted pseudo-labeling method to obtain high-confidence pseudo labels to reduce label noise ratio. Additionally, we implement a secondary LLM-guided oversampling mechanism to mitigate potential class distribution skew caused by pseudo labels. Experimental results show that GraphALP achieves superior performance over state-of-the-art methods on class-imbalanced graphs with noisy labels.
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