  Graph anomaly detection (GAD) has achieved success and has been widely
applied in various domains, such as fraud detection, cybersecurity, finance
security, and biochemistry. However, existing graph anomaly detection
algorithms focus on distinguishing individual entities (nodes or graphs) and
overlook the possibility of anomalous groups within the graph. To address this
limitation, this paper introduces a novel unsupervised framework for a new task
called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework
first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that
belong to potential anomaly groups by capturing long-range inconsistencies.
Subsequently, group sampling is employed to sample candidate groups, which are
then fed into the proposed Topology Pattern-based Graph Contrastive Learning
(TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to
generate embeddings for each candidate group and thus distinct anomaly groups.
The experimental results on both real-world and synthetic datasets demonstrate
that the proposed framework shows superior performance in identifying and
localizing anomaly groups, highlighting it as a promising solution for Gr-GAD.
Datasets and codes of the proposed framework are at the github repository
https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.
