2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
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…
Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model
Fu Lin, Xuexiong Luo, Jia Wu +4
Different from the current node-level anomaly detection task, the goal of graph-level anomaly detection is to find abnormal graphs that significantly differ from others in a graph…
Multi-representations Space Separation based Graph-level Anomaly-aware Detection
Fu Lin, Haonan Gong, Mingkang Li +3
Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem.…