We present a novel condition, which we term the net- work nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from few signal values. The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of the sampling set. Our results can be used to design efficient sampling strategies based on the network topology.
@article{arxiv.1705.04379,
title = {The Network Nullspace Property for Compressed Sensing of Big Data over Networks},
author = {Alexander Jung and Madelon Hulsebos},
journal= {arXiv preprint arXiv:1705.04379},
year = {2018}
}