DADDE:deep anomaly detection with density estimation

Abstract

The problem of abnormal nodes detection in attributed network is widely used in daily life, such as social networks, cyberspace security, financial fields and so on. Most existing detection methods ignore the relationship between structure information and attribute information in attributed network. Although some methods consider the relationship between them, but it can’t distinguish the types of abnormal nodes well, that is, attribute exceptions or structural exceptions. Aiming at the shortcomings of existing methods, this paper proposes a deep anomaly detection model combining density estimation. The idea of detecting abnormal nodes by reconstruction error is used, the structure information and attribute information of attributed network are reconstructed respectively, and abnormal nodes are detected by density estimation based on reconstruction error and embedding vector of nodes. In this paper, the validity of DADDE model is proved by experiments on three commonly used data sets in this field.