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This paper addresses the problem of segmenting a stream of graph signals: weaim to detect changes in the mean of the multivariate signal defined over thenodes of a known graph. We propose an offline algorithm that relies on theconcept of graph signal stationarity and allows the convenient translation ofthe problem from the original vertex domain to the spectral domain (GraphFourier Transform), where it is much easier to solve. Although the obtainedspectral representation is sparse in real applications, to the best of ourknowledge this property has not been much exploited in the existing relatedliterature. Our main contribution is a change-point detection algorithm thatadopts a model selection perspective, which takes into account the sparsity ofthe spectral representation and determines automatically the number ofchange-points. Our detector comes with a proof of a non-asymptotic oracleinequality, numerical experiments demonstrate the validity of our method.

Offline detection of change-points in the mean for stationary graph signals