  Although the computational and statistical trade-off for modeling single
graphs, for instance, using block models is relatively well understood,
extending such results to sequences of graphs has proven to be difficult. In
this work, we take a step in this direction by proposing two models for graph
sequences that capture: (a) link persistence between nodes across time, and (b)
community persistence of each node across time. In the first model, we assume
that the latent community of each node does not change over time, and in the
second model we relax this assumption suitably. For both of these proposed
models, we provide statistically and computationally efficient inference
algorithms, whose unique feature is that they leverage community detection
methods that work on single graphs. We also provide experimental results
validating the suitability of our models and methods on synthetic and real
instances.
