Papers
1–3 of 3Research Paper·Jan 29, 2026
AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection aims to identify abnormal patterns in networks, but faces significant challenges from label scarcity and extreme class imbalance. While graph contrastive learning offers a prom...
5.0 viability
Research Paper·Feb 5, 2026·B2BFintech
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenges persist. At the mo...
5.0 viability
Research Paper·Feb 11, 2026
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
The task of graph-level anomaly detection (GLAD) is to identify anomalous graphs that deviate significantly from the majority of graphs in a dataset. While deep GLAD methods have shown promising perfo...
5.0 viability