5 papers
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection
Wenxin Zhang, Ding Xu, Guangzhen Yao +6
Time series anomaly detection is critical for system monitoring and risk identification, across various domains, such as finance and healthcare. However, for most reconstruction-ba…
Addressing Noise and Stochasticity in Fraud Detection for Service Networks
Wenxin Zhang, Ding Xu, Xi Xuan +5
Fraud detection is crucial in social service networks to maintain user trust and improve service network security. Existing spectral graph-based methods address this challenge by l…
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
Wenxin Zhang, Xiaojian Lin, Wenjun Yu +7
Time series anomaly detection holds notable importance for risk identification and fault detection across diverse application domains. Unsupervised learning methods have become pop…
Dual-channel Heterophilic Message Passing for Graph Fraud Detection
Wenxin Zhang, Jingxing Zhong, Guangzhen Yao +4
Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task.…
Decomposition-based multi-scale transformer framework for time series anomaly detection
Wenxin Zhang, Cuicui Luo
Time series anomaly detection is crucial for maintaining stable systems. Existing methods face two main challenges. First, it is difficult to directly model the dependencies of div…