3 papers
cs.LG2025
DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
Jialun Zheng, Jie Liu, Jiannong Cao +4
Dynamic graph anomaly detection (DGAD) is essential for identifying anomalies in evolving graphs across domains such as finance, traffic, and social networks. Recently, generalist…
cs.CV2024
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
Jie Liu, Yao Wu, Xiaotong Luo +1
In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains larg…
cs.LG2024
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality
Jie Liu, Xuequn Shang, Xiaolin Han +2
Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this…