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Yutao Wei

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2 papers

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AAAI Conference 2024 Short Paper

Counterfactual Graph Learning for Anomaly Detection with Feature Disentanglement and Generation (Student Abstract)

Yutao Wei
Wenzheng Shu
Zhangtao Cheng
Wenxin Tai
Chunjing Xiao
Ting Zhong

Graph anomaly detection has received remarkable research interests, and various techniques have been employed for enhancing detection performance. However, existing models tend to learn dataset-specific spurious correlations based on statistical associations. A well-trained model might suffer from performance degradation when applied to newly observed nodes with different environments. To handle this situation, we propose CounterFactual Graph Anomaly Detection model, CFGAD. In this model, we design a gradient-based separator to disentangle node features into class features and environment features. Then, we present a weight-varying diffusion model to combine class features and environment features from different nodes to generate counterfactual samples. These counterfactual samples will be adopted to enhance model robustness. Comprehensive experiments demonstrate the effectiveness of our CFGAD.

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AAAI Conference 2024 Short Paper

Multi-Scale Dynamic Graph Learning for Time Series Anomaly Detection (Student Abstract)

Yixuan Jin
Yutao Wei
Zhangtao Cheng
Wenxin Tai
Chunjing Xiao
Ting Zhong

The success of graph neural networks (GNNs) has spurred numerous new works leveraging GNNs for modeling multivariate time series anomaly detection. Despite their achieved performance improvements, most of them only consider static graph to describe the spatial-temporal dependencies between time series. Moreover, existing works neglect the time and scale-changing structures of time series. In this work, we propose MDGAD, a novel multi-scale dynamic graph structure learning approach for time series anomaly detection. We design a multi-scale graph structure learning module that captures the complex correlations among time series, constructing an evolving graph at each scale. Meanwhile, an anomaly detector is used to combine bilateral prediction errors to detect abnormal data. Experiments conducted on two time series datasets demonstrate the effectiveness of MDGAD.

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