  Given partial measurements of a time-varying graph signal, we propose an
algorithm to simultaneously estimate both the underlying graph topology and the
missing measurements. The proposed algorithm operates by training an
interpretable neural network, designed from the unrolling framework. The
proposed technique can be used both as a graph learning and a graph signal
reconstruction algorithm. This work enhances prior work in graph signal
reconstruction by allowing the underlying graph to be unknown; and also builds
on prior work in graph learning by tailoring the learned graph to the signal
reconstruction task.
