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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
Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto
Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have in…
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…