Generative Adversarial Networks For Graph Data Imputation From Signed Observations

• Main Authors: Madapu, Amarlingam, Segarra, Santiago, Chepuri, Sundeep Prabhakar, Marques, Antonio G
• Format: Journal Article
• Language: English
• Published: 19.11.2019
• DOI: 10.48550/arxiv.1911.08447

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