5 papers
Towards One-for-All Anomaly Detection for Tabular Data
Shiyuan Li, Yixin Liu, Yu Zheng +3
Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods f…
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…
FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Yunfeng Zhao, Yixin Liu, Shiyuan Li +3
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. D…
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Yixin Liu, Shiyuan Li, Yu Zheng +3
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods…
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…