  Anomaly detection on attributed networks aims at finding nodes whose patterns
deviate significantly from the majority of reference nodes, which is pervasive
in many applications such as network intrusion detection and social spammer
detection. However, most existing methods neglect the complex cross-modality
interactions between network structure and node attribute. In this paper, we
propose a deep joint representation learning framework for anomaly detection
through a dual autoencoder (AnomalyDAE), which captures the complex
interactions between network structure and node attribute for high-quality
embeddings. Specifically, AnomalyDAE consists of a structure autoencoder and an
attribute autoencoder to learn both node embedding and attribute embedding
jointly in latent space. Moreover, attention mechanism is employed in structure
encoder to learn the importance between a node and its neighbors for an
effective capturing of structure pattern, which is important to anomaly
detection. Besides, by taking both the node embedding and attribute embedding
as inputs of attribute decoder, the cross-modality interactions between network
structure and node attribute are learned during the reconstruction of node
attribute. Finally, anomalies can be detected by measuring the reconstruction
errors of nodes from both the structure and attribute perspectives. Extensive
experiments on real-world datasets demonstrate the effectiveness of the
proposed method.
