# Distributed Cartesian Power Graph Segmentation for Graphon Estimation

@article{Wei2018DistributedCP, title={Distributed Cartesian Power Graph Segmentation for Graphon Estimation}, author={Shitong Wei and Oscar Hernan Madrid Padilla and James Sharpnack}, journal={ArXiv}, year={2018}, volume={abs/1805.09978} }

We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fused lasso (PGFL) segments a matrix by exploiting a known graphical structure, $G$, over the rows and columns. Our main results shows that for any connected graph, under subGaussian noise, the PGFL achieves the same mean-square error rate as 2D total variation denoising for signals of bounded variation. We study the use…

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