Spectrum-blind signal recovery on graphs

We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is “spectrum-blind” where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdös-Rényi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.