  In this paper, we extend the sampling theory on graphs by constructing a
framework that exploits the structure in product graphs for efficient sampling
and recovery of bandlimited graph signals that lie on them. Product graphs are
graphs that are composed from smaller graph atoms; we motivate how this model
is a flexible and useful way to model richer classes of data that can be
multi-modal in nature. Previous works have established a sampling theory on
graphs for bandlimited signals. Importantly, the framework achieves significant
savings in both sample complexity and computational complexity
