  The aim of this chapter is to give an overview of the recent advances related
to sampling and recovery of signals defined over graphs. First, we illustrate
the conditions for perfect recovery of bandlimited graph signals from samples
collected over a selected set of vertexes. Then, we describe some sampling
design criteria proposed in the literature to mitigate the effect of noise and
model mismatching when performing graph signal recovery. Finally, we illustrate
algorithms and optimal sampling strategies for adaptive recovery and tracking
of dynamic graph signals, where both sampling set and signal values are allowed
to vary with time. Numerical simulations carried out over both synthetic and
real data illustrate the potential advantages of graph signal processing
methods for sampling, interpolation, and tracking of signals observed over
irregular domains such as, e.g., technological or biological networks.
