2 papers
cs.LG2025
Meta-Learning Based Few-Shot Graph-Level Anomaly Detection
Liting Li, Yumeng Wang, Yueheng Sun
Graph-level anomaly detection aims to identify anomalous graphs or subgraphs within graph datasets, playing a vital role in various fields such as fraud detection, review classific…
cs.LG2025
Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
Zengyi Wo, Wenjun Wang, Minglai Shao +3
In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous…