  To understand the structural dynamics of a large-scale social, biological or
technological network, it may be useful to discover behavioral roles
representing the main connectivity patterns present over time. In this paper,
we propose a scalable non-parametric approach to automatically learn the
structural dynamics of the network and individual nodes. Roles may represent
structural or behavioral patterns such as the center of a star, peripheral
nodes, or bridge nodes that connect different communities. Our novel approach
learns the appropriate structural role dynamics for any arbitrary network and
tracks the changes over time. In particular, we uncover the specific global
network dynamics and the local node dynamics of a technological, communication,
and social network. We identify interesting node and network patterns such as
stationary and non-stationary roles, spikes/steps in role-memberships (perhaps
indicating anomalies), increasing/decreasing role trends, among many others.
Our results indicate that the nodes in each of these networks have distinct
connectivity patterns that are non-stationary and evolve considerably over
time. Overall, the experiments demonstrate the effectiveness of our approach
for fast mining and tracking of the dynamics in large networks. Furthermore,
the dynamic structural representation provides a basis for building more
sophisticated models and tools that are fast for exploring large dynamic
networks.
