GNAR: graph contrastive learning networks with adaptive readouts for anomaly detection

Recent advancements in graph neural networks (GNNs) have prompted diverse research endeavors focused on utilizing GNNs for anomaly detection. The fundamental concept revolves around harnessing the inherent expressive capabilities of GNNs to acquire meaningful node representations, aiming to distinguish between anomalous and normal nodes in the embedding space. However, prior methods have often employed simple readout modules (such as sum, mean, or max functions) for subgraph aggregation, failing to fully exploit subgraph information. In response to this limitation, we propose an anomaly detection application algorithm called "Graph Contrastive Learning Network with Adaptive Readouts" (GNAR), tailored specifically for Graph Anomaly Detection (GAD) tasks. Through extensive experiments on three famous public datasets, we consistently observe that GNAR outperforms baseline methods.