3 papers
cs.LG2025
GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation
Mingkang Li, Xuexiong Luo, Yue Zhang +2
Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their struct…
cs.LG2024
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature Learning
Zitong Wang, Xuexiong Luo, Enfeng Song +2
Graph-level anomaly detection (GLAD) has already gained significant importance and has become a popular field of study, attracting considerable attention across numerous downstream…
cs.LG2024
Graph Neural Networks for Brain Graph Learning: A Survey
Xuexiong Luo, Jia Wu, Jian Yang +7
Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology,…