{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Eye on AI Weekly Research Watch","title":"Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection","html":"","width":"100%","height":180,"duration":121,"description":"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.\n\nAuthors: Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi\n\nPaper: https://arxiv.org/abs/2606.28134v1","thumbnail_url":"https://img.transistorcdn.com/NzT0CgVgc5EYmTYVshPdpb6IAFKCteYvSiwlDGdBSuw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80ZDk4/YjBiMGUyYzJiNzIw/YTRjYjc4OTM2YzM4/OGQ5Ny5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}