Generative Adversarial Networks For Graph Data Imputation From Signed Observations
Signal Processing
2019-11-21 v2
Abstract
We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. However, instead of observing these signals, we observe a signed version of them and only at a subset of the nodes on the graph. Our goal is to estimate the true underlying graph signals from our observations. To achieve this, we propose a generative adversarial network (GAN) where the key is to incorporate graph-aware losses in the associated minimax optimization problem. We illustrate the benefits of the proposed method via numerical experiments on hand-written digits from the MNIST dataset
Cite
@article{arxiv.1911.08447,
title = {Generative Adversarial Networks For Graph Data Imputation From Signed Observations},
author = {Amarlingam Madapu and Santiago Segarra and Sundeep Prabhakar Chepuri and Antonio G. Marques},
journal= {arXiv preprint arXiv:1911.08447},
year = {2019}
}