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.

翻译：将图神经网络（GNN）与对比学习相结合用于异常检测，近年来引起了越来越多的关注。现有的图对比异常检测（GCAD）方法主要致力于通过图增强和多尺度对比模块来提升检测能力。然而，这些模块如何工作的内在机制尚未得到充分探索。我们深入研究了多尺度和图增强机制，并观察到多尺度对比模块并未增强表示能力，而多GNN模块才是隐藏的贡献者。以往的研究往往将多GNN带来的益处归因于多尺度模块。在本文中，我们探究了这一误解，并提出了多GNN与增强图对比框架MAG，该框架从对比自监督的角度统一了现有的GCAD方法。我们从MAG框架中提取了两个变体：L-MAG和M-MAG。L-MAG是MAG的轻量级实例，在Cora和Pubmed上以较低计算成本超越了现有最佳方法。配备了多GNN模块的变体M-MAG进一步提升了检测性能。我们的研究揭示了现有GCAD方法中的缺陷，并展示了多GNN和图增强模块的潜力。我们的代码可在https://github.com/liuyishoua/MAG-Framework获取。