5 papers
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…
Boundary-enhanced time series data imputation with long-term dependency diffusion models
Chunjing Xiao, Xue Jiang, Xianghe Du +4
Data imputation is crucial for addressing challenges posed by missing values in multivariate time series data across various fields, such as healthcare, traffic, and economics, and…
Diffusion Model-based Contrastive Learning for Human Activity Recognition
Chunjing Xiao, Yanhui Han, Wei Yang +3
WiFi Channel State Information (CSI)-based activity recognition has sparked numerous studies due to its widespread availability and privacy protection. However, when applied in pra…
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…
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…