  The last two decades have seen a tremendous surge in research on social
networks and their implications. The studies includes inferring social
relationships, which in turn have been used for target advertising,
recommendations, search customization etc. However, the offline experiences of
human, the conversations with people and face-to-face interactions that govern
our lives interactions have received lesser attention. We introduce DAIICT
Spatio-Temporal Network (DSSN), a spatiotemporal dataset of 0.7 million data
points of continuous location data logged at an interval of every 2 minutes by
mobile phones of 46 subjects. Our research is focused at inferring relationship
strength between students based on the spatiotemporal data and comparing the
results with the self-reported data. In that pursuit we introduce Temporal
Diversity, which we show to be superior in its contribution to predicting
relationship strength than its counterparts. We also explore the evolving
nature of Temporal Diversity with time. Our rich dataset opens various other
avenues of research that require fine-grained location data with bounded
movement of participants within a limited geographical area. The advantage of
having a bounded geographical area such as a university campus is that it
provides us with a microcosm of the real world, where each such geographic zone
has an internal context and function and a high percentage of mobility is
governed by schedules and time-tables. The bounded geographical region in
addition to the age homogeneous population gives us a minute look into the
active internal socialization of students in a university.
