egoStellar: Visual Analysis of Anomalous Communication Behaviors from Egocentric Perspective

Detection and analysis of anomalous communication behaviors in cellar networks are extremely important in identifying potential advertising agency or fraud users. Visual analytics benefits domain experts in this problem for its intuitiveness and friendly interactive interface in presenting and exploring large volumes of data. In this paper, we propose a visual analytics system, egoStellar, to interactively explore the communication behaviors of mobile users from an ego network perspective. Ego network is composed of a centered individual and the relationships between the ego and his/her direct contacts (alters). Based on the graph model, egoStellar presents an overall statistical view to explore the distribution of mobile users for behavior inspection, a group view to classify the users and extract features for anomalous detection and comparison, and a ego-centric view to show the interactions between an ego and the alters in details. Our system can help analysts to interactively explore the communication patterns of mobile users from egocentric perspectives. Thus, this system makes it easier for the government or operators to visually inspect the massive communication behaviors in a intuitive way to detect and analyze anomalous users. Furthermore, our design can provide the researchers a good opportunity to observe the personal communication patterns to uncover new knowledge about human social interactions. Our proposed design can be applied to other fields where network structure exists. We evaluated egoStellar with real datasets containing the anomalous users with extremely large contacts in a short time period. The results show our system is effective in identifying anomalous communication behaviors, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data.