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Found 3 papers, 2 papers with code
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.
ER: Equivariance Regularizer for Knowledge Graph Completion
To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information.
Geometry Interaction Knowledge Graph Embeddings
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks.
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