6 papers · 1 filter
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.…
Dual Boost-Driven Graph-Level Clustering Network
John Smith, Wenxuan Tu, Junlong Wu +10
Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notabl…
Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering
Renda Han, Guangzhen Yao, Wenxin Zhang +7
Graph-level clustering is a fundamental task of data mining, aiming at dividing unlabeled graphs into distinct groups. However, existing deep methods that are limited by pooling ha…