Graph Neural Networks (GNNs) exhibit strong potential in node classification
task through a message-passing mechanism. However, their performance often
hinges on high-quality node labels, which are challenging to obtain in
real-world scenarios due to unreliable sources or adversarial attacks.
Consequently, label noise is common in real-world graph data, negatively
impacting GNNs by propagating incorrect information during training. To address
this issue, the study of Graph Neural Networks under Label Noise (GLN) has
recently gained traction. However, due to variations in dataset selection, data
splitting, and preprocessing techniques, the community currently lacks a
comprehensive benchmark, which impedes deeper understanding and further
development of GLN. To fill this gap, we introduce NoisyGL in this paper, the
first comprehensive benchmark for graph neural networks under label noise.
NoisyGL enables fair comparisons and detailed analyses of GLN methods on noisy
labeled graph data across various datasets, with unified experimental settings
and interface. Our benchmark has uncovered several important insights that were
missed in previous research, and we believe these findings will be highly
beneficial for future studies. We hope our open-source benchmark library will
foster further advancements in this field. The code of the benchmark can be
found in this https URL

本研究提出了第一个面向噪声标签下图神经网络的全面基准 NoisyGL，以允许对噪声标签下图数据上的 GLN 方法进行公平比较和详细分析，并揭示了几个在先前研究中被忽视的重要见解。