  We study the problem of sampling and reconstruction of bandlimited graph
signals where the objective is to select a node subset of prescribed
cardinality that ensures interpolation of the original signal with the lowest
reconstruction error. We propose an efficient iterative selection sampling
approach and show that in the noiseless case the original signal is exactly
recovered from the set of selected nodes. In the case of noisy measurements, a
bound on the reconstruction error of the proposed algorithm is established. We
further address the support identification of the bandlimited signal with
unknown support and show that under a pragmatic sufficient condition, the
proposed framework requires minimal number of samples to perfectly identify the
support. The efficacy of the proposed methods are illustrated through numerical
simulations on synthetic and real-world graphs.
