New method tackles noisy labels in Graph Neural Networks

Researchers have developed a new method called ICGNN to improve the robustness of Graph Neural Networks (GNNs) when dealing with noisy labels. The approach uses a novel noise indicator, the influence contradiction score (ICS), derived from graph diffusion, to identify potentially mislabeled nodes. A Gaussian mixture model is then employed for precise noise detection, followed by a soft strategy to correct these labels using neighboring node predictions. The method also incorporates pseudo-labeling to enhance supervision signals for unlabeled nodes, with experiments demonstrating its effectiveness over existing baselines. AI

影响 Improves the reliability of GNNs in real-world applications by addressing data quality issues.

排序理由 This is a research paper detailing a novel method for improving GNNs. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Graph Neural Networks
• ICGNN
• Wei Ju

• 论文
• 其他

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