10 citations · 10 across the 7 of their papers we have counts for
4 papers · 1 filter
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Rui Miao +5
Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly autonomous in various safety-critical…
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
Junjun Pan, Yixin Liu, Chuan Zhou +3
Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume ide…
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…
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng +4
Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic conn…