8 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 8 cited
Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection
Chunjing Xiao, Jiahui Lu, Xovee Xu +4
Graph anomaly detection is critical in domains such as healthcare and economics, where identifying deviations can prevent substantial losses. Existing unsupervised approaches striv…
cs.LG2024★ 5 cited
Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection
Chunjing Xiao, Shikang Pang, Wenxin Tai +3
Graph-level anomaly detection is significant in diverse domains. To improve detection performance, counterfactual graphs have been exploited to benefit the generalization capacity…
cs.LG2024
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
Chunjing Xiao, Shikang Pang, Xovee Xu +3
A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the repres…