Interactions among individuals are often modeled as social networks
where nodes represent individuals and an edge exists if the
corresponding individuals have interacted during
the observation period. The model is essentially static in that the
interactions are aggregated over time and all information about the time
and ordering of social interactions is discarded. We show that such
traditional social network analysis methods may result in incorrect
conclusions on dynamic data about the structure of interactions and the
processes that spread over those interactions.

We have extended computational methods for social network analysis to
explicitly address the dynamic nature of interactions among individuals.
We have developed techniques for identifying persistent communities,
influential individuals, and extracting patterns of interactions in
dynamic social networks. We will discuss computational properties of the
analysis problems and algorithms for solving them. Time permitting, we
will demonstrate the applicability of the techniques by analyzing zebra
social networks.