  From social networks to Internet applications, a wide variety of electronic
communication tools are producing streams of graph data; where the nodes
represent users and the edges represent the contacts between them over time.
This has led to an increased interest in mechanisms to model the dynamic
structure of time-varying graphs. In this work, we develop a framework for
learning the latent state space of a time-varying email graph. We show how the
framework can be used to find subsequences that correspond to global real-time
events in the Email graph (e.g. vacations, breaks, ...etc.). These events
impact the underlying graph process to make its characteristics non-stationary.
Within the framework, we compare two different representations of the temporal
relationships; discrete vs. probabilistic. We use the two representations as
inputs to a mixture model to learn the latent state transitions that correspond
to important changes in the Email graph structure over time.
