  Many real-world networks are complex dynamical systems, where both local
(e.g., changing node attributes) and global (e.g., changing network topology)
processes unfold over time. Local dynamics may provoke global changes in the
network, and the ability to detect such effects could have profound
implications for a number of real-world problems. Most existing techniques
focus individually on either local or global aspects of the problem or treat
the two in isolation from each other. In this paper we propose a novel network
model that simultaneously accounts for both local and global dynamics. To the
best of our knowledge, this is the first attempt at modeling and detecting
local and global change points on dynamic networks via a unified generative
framework. Our model is built upon the popular mixed membership stochastic
blockmodels (MMSB) with sparse co-evolving patterns. We derive an efficient
stochastic gradient Langevin dynamics (SGLD) sampler for our proposed model,
which allows it to scale to potentially very large networks. Finally, we
validate our model on both synthetic and real-world data and demonstrate its
superiority over several baselines.
