An embedding approach to anomaly detection

Network anomaly detection has become very popular in recent years because of the importance of discovering key regions of structural inconsistency in the network. In addition to application-specific information carried by anomalies, the presence of such structural inconsistency is often an impediment to the effective application of data mining algorithms such as community detection and classification. In this paper, we study the problem of detecting structurally inconsistent nodes that connect to a number of diverse influential communities in large social networks. We show that the use of a network embedding approach, together with a novel dimension reduction technique, is an effective tool to discover such structural inconsistencies. We also experimentally show that the detection of such anomalous nodes has significant applications: one is the specific use of detected anomalies, and the other is the improvement of the effectiveness of community detection.