"DAGAD: Data Augmentation for Graph Anomaly Detection" by Fanzhen Liu, Xiaoxiao Ma et al.
Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of backgrou...
Data Augmentation Based Graph Anomaly Detection Anomalous Sample Scarcity Anomaly Detection Class Imbalance Data Augmentation Graph Mining
Source: uow.edu.au