Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection

A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable.

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Cite this as

Chunjing Xiao, Shikang Pang, Xovee Xu, Xuan Li, Goce Trajcevski, Fan Zhou (2024). Dataset: Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection. https://doi.org/10.57702/7r7312uz

DOI retrieved: December 3, 2024

Additional Info

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2407.02143
Author Chunjing Xiao
More Authors
Shikang Pang
Xovee Xu
Xuan Li
Goce Trajcevski
Fan Zhou
Homepage https://github.com/ChunjingXiao/CAGAD