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What is Leveraging Peer-Informed Label Consistency about?

The paper introduces ProCon, a novel framework for enhancing the robustness of Graph Neural Networks (GNNs) against noisy labels by leveraging peer-informed label consistency. It identifies mislabeled nodes by measuring label consistency among semantically similar peers and utilizes a Gaussian Mixture Model to distinguish clean samples, significantly outperforming existing methods in experiments. The approach effectively mitigates label noise and improves GNN reliability across various applications.

Author

assemalkaddoour

Language

EN