Detection and Characterization of Anomalous Entities in Social Communication Networks

Social networks generated from emails or calls provide enormous geospatial and interaction information about subscribers. These have served as important inputs to intelligence analysts. In this paper, we propose an efficient algorithm for anomaly detection from social networks. Anomalous users are detected based on their behavioral dissimilarity from others. A rich feature set is proposed for outlier detection. A method for providing visual explanation for the results is also proposed.