Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms. Despite increasing efforts towards improving the quality of generic data types, such as images and texts, the problem of mislabel detection in graph data remains underexplored. To bridge the gap, we explore mislabelling issues in popular real-world graph datasets and propose GraphCleaner, a post-hoc method to detect and correct these mislabelled nodes in graph datasets. GraphCleaner combines the novel ideas of 1) Synthetic Mislabel Dataset Generation, which seeks to generate realistic mislabels; and 2) Neighborhood-Aware Mislabel Detection, where neighborhood dependency is exploited in both labels and base classifier predictions. Empirical evaluations on 6 datasets and 6 experimental settings demonstrate that GraphCleaner outperforms the closest baseline, with an average improvement of 0.14 in F1 score, and 0.16 in MCC. On real-data case studies, GraphCleaner detects real and previously unknown mislabels in popular graph benchmarks: PubMed, Cora, CiteSeer and OGB-arxiv; we find that at least 6.91% of PubMed data is mislabelled or ambiguous, and simply removing these mislabelled data can boost evaluation performance from 86.71% to 89.11%.

本文提出了解决图数据中误标签的问题的方法，GraphCleaner，通过合成假标签数据集和基于邻域依赖性的误标签检测技术，该方法在6个数据集和6个实验设置中得到了显著的平均F1得分为0.14和MCC为0.16的改善，并在PubMed，Cora，CiteSeer和OGB-arxiv等受欢迎的图形基准测试中检测到真实和以前不知道的错误标签，其中至少有6.91％的PubMed数据是误标记或模糊的。

GraphCleaner: 检测在流行的图学习基准中错误标记的样本