  In the era of mobile computing, understanding human mobility patterns is
crucial in order to better design protocols and applications. Many studies
focus on different aspects of human mobility such as people's points of
interests, routes, traffic, individual mobility patterns, among others. In this
work, we propose to look at human mobility through a social perspective, i.e.,
analyze the impact of social groups in mobility patterns. We use the MIT
Reality Mining proximity trace to detect, track and investigate group's
evolution throughout time. Our results show that group meetings happen in a
periodical fashion and present daily and weekly periodicity. We analyze how
groups' dynamics change over day hours and find that group meetings lasting
longer are those with less changes in members composition and with members
having stronger social bonds with each other. Our findings can be used to
propose meeting prediction algorithms, opportunistic routing and information
diffusion protocols, taking advantage of those revealed properties.
