Latent Representation Learning for Attributed Graph Anomaly Detection

Anomaly detection in attributed graph data has been widely applied in real applications. However, the intricate topology of graph data, high-dimensional attributes, and class imbalance inherent in anomaly detection tasks render attributed graph anomaly detection a challenging task. To detect anomalies using the intricate topology information of graph data, a dual-masked autoencoders is proposed for attributed graph anomaly detection, denoted as MAGAD. Specifically, in the MAGAD, the class imbalance in attributed graph data is dealt with by randomly masking the original graph data to obtain masked graph data for the anomaly detection task. And then, a latent representation of the graph data is obtained by training dual autoencoders, where one autoencoder is developed for reconstructing the original graph data, and another for reconstructing randomly masked graph data. This assists in identifying abnormal nodes in the attributed graph data. Subsequently, to capture anomalous information from relevant features, MAGAD uses a random re-masking strategy for latent representations learned from the masked graph. Finally, the anomaly scores of the nodes are calculated using the learned latent representations from the decoders of the dual autoencoders. Experimental results on five real-world datasets demonstrate that the MAGAD algorithm outperforms state-of-the-art anomaly detection algorithms.