1EVENT DETECTION IN TIME SERIES OF MOBILE COMMUNICATION GRAPHS

Anomaly and event detection has been studied widely for having many applications in fraud detection, network intrusion detection, detection of epidemic outbreaks, and so on. In this paper we propose an algorithm that operates on a time-varying network of agents with edges representing interactions between them and (1) spots "anomalous " points in time at which many agents "change " their behavior in a way it deviates from the norm; and (2) attributes the detected anomaly to those agents that contribute to the "change " the most. Experiments on a large mobile phone network (of 2 million anonymous customers with 50 million interactions over a period of 6 months) shows that the "change"-points detected by our algorithm coincide with the social events and the festivals in our data. 1