3 papers
cs.LG2026
ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes
Ziyan Wang, Liwen Wu, Cheng Xie +3
Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanni…
cs.LG2026
TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection
Xiong Zhang, Hong Peng, Changlong Fu +3
A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of th…
cs.SI2026
GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection
Xiong Zhang, Hong Peng, Zhenli He +3
Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomal…