  This paper addresses the problem of segmenting a stream of graph signals: we
aim to detect changes in the mean of a multivariate signal defined over the
nodes of a known graph. We propose an offline method that relies on the concept
of graph signal stationarity and allows the convenient translation of the
problem from the original vertex domain to the spectral domain (Graph Fourier
Transform), where it is much easier to solve. Although the obtained spectral
representation is sparse in real applications, to the best of our knowledge
this property has not been sufficiently exploited in the existing related
literature. Our change-point detection method adopts a model selection approach
that takes into account the sparsity of the spectral representation and
determines automatically the number of change-points. Our detector comes with a
proof of a non-asymptotic oracle inequality. Numerical experiments demonstrate
the performance of the proposed method.
