  The complex topology of real networks allows its actors to change their
functional behavior. Network models provide better understanding of the
evolutionary mechanisms being accountable for the growth of such networks by
capturing the dynamics in the ways network agents interact and change their
behavior. Considerable amount of research efforts is required for developing
novel network modeling techniques to understand the structural properties such
networks, reproducing similar properties based on empirical evidence, and
designing such networks efficiently. First, we demonstrate how to construct
social interaction networks using social media data and then present the key
findings obtained from the network analytics. We analyze the characteristics
and growth of such interaction networks, examine the network properties and
derive important insights based on the theories of network science literature.
We also discuss the application of such networks as a useful tool to
effectively disseminate targeted information during planned special events. We
observed that the degree-distributions of such networks follow power-law that
is indicative of the existence of fewer nodes in the network with higher levels
of interactions, and many other nodes with less interactions. While the network
elements and average user degree grow linearly each day, densities of such
networks tend to become zero. Largest connected components exhibit higher
connectivity (density) when compared with the whole graph. Network radius and
diameter become stable over time evidencing the small-world property. We also
observe increased transitivity and higher stability of the power-law exponents
as the networks grow. Data is specific to the Purdue University community and
two large events, namely Purdue Day of Giving and Senator Bernie Sanders' visit
to Purdue University as part of Indiana Primary Election 2016.
