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Label errors have been found to be prevalent in popular text, vision, andaudio datasets, which heavily influence the safe development and evaluation ofmachine learning algorithms. Despite increasing efforts towards improving thequality of generic data types, such as images and texts, the problem ofmislabel detection in graph data remains underexplored. To bridge the gap, weexplore mislabelling issues in popular real-world graph datasets and proposeGraphCleaner, a post-hoc method to detect and correct these mislabelled nodesin graph datasets. GraphCleaner combines the novel ideas of 1) SyntheticMislabel Dataset Generation, which seeks to generate realistic mislabels; and2) Neighborhood-Aware Mislabel Detection, where neighborhood dependency isexploited in both labels and base classifier predictions. Empirical evaluationson 6 datasets and 6 experimental settings demonstrate that GraphCleaneroutperforms the closest baseline, with an average improvement of 0.14 in F1score, and 0.16 in MCC. On real-data case studies, GraphCleaner detects realand previously unknown mislabels in popular graph benchmarks: PubMed, Cora,CiteSeer and OGB-arxiv; we find that at least 6.91% of PubMed data ismislabelled or ambiguous, and simply removing these mislabelled data can boostevaluation performance from 86.71% to 89.11%.

GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks