1 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
GraphPI: Efficient Protein Inference with Graph Neural Networks
Zheng Ma, Jiazhen Chen, Lei Xin +1
The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application i…
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…
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…
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
Katrina Chen, Mingbin Feng, Tony S. Wirjanto
Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in…