DeGLIF identifies noisy graph nodes by approximating how much each node's removal would improve loss on a small clean set, relabels them with the model's most confident alternative class, and retrains, improving accuracy under symmetric and pairwise label noise.
In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)
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DeGLIF for Label Noise Robust Node Classification using GNNs
DeGLIF identifies noisy graph nodes by approximating how much each node's removal would improve loss on a small clean set, relabels them with the model's most confident alternative class, and retrains, improving accuracy under symmetric and pairwise label noise.