Adversarial regularized attributed network embedding for graph anomaly detection

Graph anomaly detection aims to identify the nodes that display significantly different behavior from the majority. However, existing methods neglect the combined interaction between the network structure and node attributes, resulting in suboptimal latent representations of nodes due to network noise. In this paper, we introduce a novel approach called adversarial regularized attributed network embedding (ARANE) for graph anomaly detection. ARANE addresses this issue by forcing normal nodes to inhabit a compact manifold in the latent space, taking into account both the network structure and node attributes.It ensures that data points from the normal class, originating from different distributions, are distributed within a single compact latent space, while excluding anomalies from this region.ARANE employs a dual-encoder architecture consisting of an attribute encoder and a structure encoder.The attribute encoder learns node attribute embeddings, while the structure encoder focuses on learning structure embeddings.To obtain high-quality node embeddings for effective anomaly detection, we apply adversarial learning to regularize the learned embeddings separately in both the structure and attribute spaces.Furthermore, we introduce a fusion module that combines the final node embeddings derived from the structure and attribute spaces.These joint embeddings serve as inputs to a dual-decoder for graph reconstruction, where the resulting reconstruction errors are utilized as anomaly scores for anomaly detection.Extensive experiments conducted on real-world attributed networks demonstrate the superior effectiveness of our proposed method compared to state-of-the-art approaches.