  Combining Graph neural networks (GNNs) with contrastive learning for anomaly
detection has drawn rising attention recently. Existing graph contrastive
anomaly detection (GCAD) methods have primarily focused on improving detection
capability through graph augmentation and multi-scale contrast modules.
However, the underlying mechanisms of how these modules work have not been
fully explored. We dive into the multi-scale and graph augmentation mechanism
and observed that multi-scale contrast modules do not enhance the expression,
while the multi-GNN modules are the hidden contributors. Previous studies have
tended to attribute the benefits brought by multi-GNN to the multi-scale
modules. 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. We extracted two
variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight
instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed
with the low computational cost. The variant M-MAG equipped with multi-GNN
modules further improve the detection performance. Our study sheds light on the
drawback of the existing GCAD methods and demonstrates the potential of
multi-GNN and graph augmentation modules. Our code is available at
https://github.com/liuyishoua/MAG-Framework.
