  Graph anomaly detection (GAD) is a vital task in graph-based machine learning
and has been widely applied in many real-world applications. The primary goal
of GAD is to capture anomalous nodes from graph datasets, which evidently
deviate from the majority of nodes. Recent methods have paid attention to
various scales of contrastive strategies for GAD, i.e., node-subgraph and
node-node contrasts. However, they neglect the subgraph-subgraph comparison
information which the normal and abnormal subgraph pairs behave differently in
terms of embeddings and structures in GAD, resulting in sub-optimal task
performance. In this paper, we fulfill the above idea in the proposed
multi-view multi-scale contrastive learning framework with subgraph-subgraph
contrast for the first practice. To be specific, we regard the original input
graph as the first view and generate the second view by graph augmentation with
edge modifications. With the guidance of maximizing the similarity of the
subgraph pairs, the proposed subgraph-subgraph contrast contributes to more
robust subgraph embeddings despite of the structure variation. Moreover, the
introduced subgraph-subgraph contrast cooperates well with the widely-adopted
node-subgraph and node-node contrastive counterparts for mutual GAD performance
promotions. Besides, we also conduct sufficient experiments to investigate the
impact of different graph augmentation approaches on detection performance. The
comprehensive experimental results well demonstrate the superiority of our
method compared with the state-of-the-art approaches and the effectiveness of
the multi-view subgraph pair contrastive strategy for the GAD task.
