4 papers · 1 filter
SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection
Kehan Yan, Yue Tan, Qingfeng Chen +3
Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing anomalous tokens within text.…
PaSta: Noisy Node Classification with Partial Label Learning
Yujing Liu, Yixin Liu, Yu Zheng +3
Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak s…
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
Yixin Liu, Shiyuan Li, Yu Zheng +4
Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…
A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Models
Junjun Pan, Yu Zheng, Yue Tan +1
Graph anomaly detection (GAD) has attracted increasing attention in recent years for identifying malicious samples in a wide range of graph-based applications, such as social media…