  It has been shown recently that graph signals with small total variation can
be accurately recovered from only few samples if the sampling set satisfies a
certain condition, referred to as the network nullspace property. Based on this
recovery condition, we propose a sampling strategy for smooth graph signals
based on random walks. Numerical experiments demonstrate the effectiveness of
this approach for graph signals obtained from a synthetic random graph model as
well as a real-world dataset.
