  Graph-level anomaly detection (GLAD) has already gained significant
importance and has become a popular field of study, attracting considerable
attention across numerous downstream works. The core focus of this domain is to
capture and highlight the anomalous information within given graph datasets. In
most existing studies, anomalies are often the instances of few. The stark
imbalance misleads current GLAD methods to focus on learning the patterns of
normal graphs more, further impacting anomaly detection performance. Moreover,
existing methods predominantly utilize the inherent features of nodes to
identify anomalous graph patterns which is approved suboptimal according to our
experiments. In this work, we propose an imbalanced GLAD method via
counterfactual augmentation and feature learning. Specifically, we first
construct anomalous samples based on counterfactual learning, aiming to expand
and balance the datasets. Additionally, we construct a module based on Graph
Neural Networks (GNNs), which allows us to utilize degree attributes to
complement the inherent attribute features of nodes. Then, we design an
adaptive weight learning module to integrate features tailored to different
datasets effectively to avoid indiscriminately treating all features as
equivalent. Furthermore, extensive baseline experiments conducted on public
datasets substantiate the robustness and effectiveness. Besides, we apply the
model to brain disease datasets, which can prove the generalization capability
of our work. The source code of our work is available online.
