2018/07/18 by Abolfazl Hashemi, Hashemi, Abolfazl, Rasoul Shafipour +5
Computer Science · Biochemistry, Genetics and Molecular Biology · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks
paper · pdf · doi:10.48550/arxiv.1807.07184
We study the problem of sampling and reconstruction of bandlimited graph\nsignals where the objective is to select a node subset of prescribed\ncardinality that ensures interpolation of the original signal with the lowest\nreconstruction error. We propose an efficient iterative selection sampling\napproach and show that in the noiseless case the original signal is exactly\nrecovered from the set of selected nodes. In the case of noisy measurements, a\nbound on the reconstruction error of the proposed algorithm is established. We\nfurther address the support identification of the bandlimited signal with\nunknown support and show that under a pragmatic sufficient condition, the\nproposed framework requires minimal number of samples to perfectly identify the\nsupport. The efficacy of the proposed methods are illustrated through numerical\nsimulations on synthetic and real-world graphs.\n