221 citations · 341 across the 7 of their papers we have counts for
7 papers
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
Junjun Pan, Yixin Liu, Yu Zheng +3
Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, frau…
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
Yu Zheng, Huan Yee Koh, Ming Jin +6
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial pro…
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Yu Zheng, Huan Yee Koh, Ming Jin +5
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approach…
eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation
Lu Yu, Wei Xiang, Juan Fang +2
Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover,…
Web of Scholars: A Scholar Knowledge Graph
Jiaying Liu, Jing Ren, Wenqing Zheng +3
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scho…
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Yu Zheng, Ming Jin, Yixin Liu +3
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detectio…
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Yu Zheng, Ming Jin, Yixin Liu +3
Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Ex…