DPF-GFD uses complementary frequency filtering on the original graph and a similarity graph to produce more discriminative node embeddings for fraud detection under high heterophily and class imbalance.
Graph attention networks
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MPAIACL applies contrastive learning to generate adversarial invariant augmentations that improve GNN generalization under covariate shifts on graphs.
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Graph-Based Fraud Detection with Dual-Path Graph Filtering
DPF-GFD uses complementary frequency filtering on the original graph and a similarity graph to produce more discriminative node embeddings for fraud detection under high heterophily and class imbalance.
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Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
MPAIACL applies contrastive learning to generate adversarial invariant augmentations that improve GNN generalization under covariate shifts on graphs.