Zishun Rui

MEGAD: A Memory-Efficient Framework for Large-Scale Attributed Graph Anomaly Detection

Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectivenes