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

Financial fraud detection in transaction networks faces a fundamental challenge: fraudulent activity is rare, well-disguised, and often underrepresented in labeled data. Standard graph neural networks tend to smooth out the very irregularities that signal fraud. ADC-GNN tackles this with three complementary mechanisms: diffusion-guided feature augmentation that stabilizes node representations against noise, contrastive learning across perturbed views, and a spectral attention module that adaptively amplifies fraud-relevant frequency signals across multiple graph hops. Evaluated on public benchmarks and a real telecom transaction dataset, it consistently outperforms baselines under low-label conditions. Applications include credit card fraud detection, anti-money laundering systems, telecommunications billing abuse detection, and social network spam identification. Authors: Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi Paper: https://arxiv.org/abs/2606.28134v1