  In graph signal processing (GSP), prior information on the dependencies in
the signal is collected in a graph which is then used when processing or
analyzing the signal. Blind source separation (BSS) techniques have been
developed and analyzed in different domains, but for graph signals the research
on BSS is still in its infancy. In this paper, this gap is filled with two
contributions. First, a nonparametric BSS method, which is relevant to the GSP
framework, is refined, the Cram\'{e}r-Rao bound (CRB) for mixing and unmixing
matrix estimators in the case of Gaussian moving average graph signals is
derived, and for studying the achievability of the CRB, a new parametric method
for BSS of Gaussian moving average graph signals is introduced. Second, we also
consider BSS of non-Gaussian graph signals and two methods are proposed.
Identifiability conditions show that utilizing both graph structure and
non-Gaussianity provides a more robust approach than methods which are based on
only either graph dependencies or non-Gaussianity. It is also demonstrated by
numerical study that the proposed methods are more efficient in separating
non-Gaussian graph signals.
