3 papers
cs.LG2025
Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection
Jiazhen Chen, Xiuqin Liang, Sichao Fu +2
Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node informati…
cs.LG2025
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs
Jiazhen Chen, Sichao Fu, Zheng Ma +3
Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate…
cs.LG2024
Towards Cross-domain Few-shot Graph Anomaly Detection
Jiazhen Chen, Sichao Fu, Zhibin Zhang +4
Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance o…