  The majority of real-world networks are dynamic and extremely large (e.g.,
Internet Traffic, Twitter, Facebook, ...). To understand the structural
behavior of nodes in these large dynamic networks, it may be necessary to model
the dynamics of behavioral roles representing the main connectivity patterns
over time. In this paper, we propose a dynamic behavioral mixed-membership
model (DBMM) that captures the roles of nodes in the graph and how they evolve
over time. Unlike other node-centric models, our model is scalable for
analyzing large dynamic networks. In addition, DBMM is flexible,
parameter-free, has no functional form or parameterization, and is
interpretable (identifies explainable patterns). The performance results
indicate our approach can be applied to very large networks while the
experimental results show that our model uncovers interesting patterns
underlying the dynamics of these networks.
