5 papers
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…
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…
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…
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
Zheng Ma, Zeping Mao, Ruixue Zhang +5
Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquis…
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…