  This work aims at recovering signals that are sparse on graphs. Compressed
sensing offers techniques for signal recovery from a few linear measurements
and graph Fourier analysis provides a signal representation on graph. In this
paper, we leverage these two frameworks to introduce a new Lasso recovery
algorithm on graphs. More precisely, we present a non-convex, non-smooth
algorithm that outperforms the standard convex Lasso technique. We carry out
numerical experiments on three benchmark graph datasets.
