Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection

arXiv:2606.28134v1 Announce Type: new Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks. Real-world fraud graphs pose two coupled challenges: sparse and imbalanced supervision, where verified fraudulent labels are scarce and heavily skewed toward benign accounts, and representation dilution, where spatial message passing may oversmooth camouflaged anomalies while spectral filters may suppress fraud-relevant mid- and high-frequency irregularities. To address

Source: arXiv cs.LG — read the full report at the original publisher.

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