  To analyze data supported by arbitrary graphs G, DSP has been extended to
Graph Signal Processing (GSP) by redefining traditional DSP concepts like
shift, filtering, and Fourier transform among others. This paper revisits
modulation, convolution, and sampling of graph signals as appropriate natural
extensions of the corresponding DSP concepts. To define these for both the
vertex and the graph frequency domains, we associate with generic data graph G
and its graph shift A, a graph spectral shift M and a spectral graph Gs. This
leads to a spectral GSP theory that parallels in the graph frequency domain the
existing GSP theory in the vertex domain. The paper applies this to design and
recovery sampling techniques for data supported by arbitrary directed graphs.
