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Revisiting Graph Contrastive Learning for Anomaly Detection
In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective.
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information
For a more comprehensive conclusion, we further investigate the effect of the objective function and the number of fused views on detection performance.
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