  Using projections on the (generalized) eigenvectors associated to matrices
that characterize the topological structure, several authors have constructed
generalizations of the Fourier transform on graphs. By exploring mappings of
the spectrum of these matrices we show how to construct more general
transforms, in particular wavelet-like transforms on graphs. For time-series,
tomograms, a generalization of the Radon transforms to arbitrary pairs of
non-commuting operators, are positive bilinear transforms with a rigorous
probabilistic interpretation which provide a full characterization of the
signals and are robust in the presence of noise. Here the notion of tomogram
transform is also extended to signals on arbitrary graphs
