Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information

Understand

Anomaly detection is defined as discovering patterns that do not conform to the expected behavior.

• Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement.
• As the emergence of graph neural networks (GNN), graph anomaly detection has been greatly developed.
• However, recent studies have shown that GNN-based methods encounter challenge, in that no graph anomaly detection algorithm can perform generalization on most datasets.