3 papers
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…
cs.LG2024
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
Xiong Zhang, Cheng Xie, Haoran Duan +1
Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream lear…