Title: Social Events in a Time-Varying Mobile Phone Graph,
Abstract: The large-scale study of human mobility has been significantly enhanced over
the last decade by the massive use of mobile phones in urban populations.
Studying the activity of mobile phones allows us, not only to infer social
networks between individuals, but also to observe the movements of these
individuals in space and time. In this work, we investigate how these two
related sources of information can be integrated within the context of
detecting and analyzing large social events. We show that large social events
can be characterized not only by an anomalous increase in activity of the
antennas in the neighborhood of the event, but also by an increase in social
relationships of the attendants present in the event. Moreover, having detected
a large social event via increased antenna activity, we can use the network
connections to infer whether an unobserved user was present at the event. More
precisely, we address the following three challenges: (i) automatically
detecting large social events via increased antenna activity; (ii)
characterizing the social cohesion of the detected event; and (iii) analyzing
the feasibility of inferring whether unobserved users were in the event.