Summary of Counterfactual Data Augmentation with Denoising Diffusion For Graph Anomaly Detection, by Chunjing Xiao et al.
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
by Chunjing Xiao, Shikang Pang, Xovee Xu, Xuan Li, Goce Trajcevski, Fan Zhou
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, CAGAD, addresses the issue of anomaly detection in Graph Neural Networks (GNNs) by introducing an unsupervised counterfactual data augmentation approach. The method involves a graph pointer neural network that identifies potential anomalies with normal-node-dominant neighborhoods. For each identified anomaly, a graph-specific diffusion model translates part of its neighbors into anomalous ones, which are then used to produce more distinguishable counterfactual representations. The results demonstrate significant improvements in F1, AUC-ROC, and AUC-PR on four datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAGAD is a new way to make Graph Neural Networks better at finding anomalies. When GNNs try to find abnormal nodes, the normal nodes around them can hide their unusual features. To fix this, CAGAD uses a special kind of neural network that identifies potential anomalies and then changes some of the normal nodes nearby into fake anomalies. This helps the GNN learn more about what makes those abnormal nodes different. The results show that CAGAD works really well, with big improvements in finding anomalies on four different datasets. |
Keywords
» Artificial intelligence » Anomaly detection » Auc » Data augmentation » Diffusion model » Gnn » Neural network » Unsupervised
