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Learning Graph Neural Networks with Noisy Labels
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We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task. Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.
Forward citations
Cited by 3 Pith papers
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Towards Trustworthy Hypergraph Neural Networks under Label Noise
A benchmark and a robust framework, HyperTrust, showing that entropy-based hyperedge trustworthiness with selective edge boosting and pruning improves hypergraph node classification under label noise.
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Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
ICGNN detects noisy node labels with a diffusion-based influence-contradiction score, fits a GMM to split clean from noisy nodes, softly corrects them using neighbor predictions, and adds pseudo-labels for unlabeled nodes.
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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 accur...
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