  We present Mask-GVAE, a variational generative model for blind denoising
large discrete graphs, in which "blind denoising" means we don't require any
supervision from clean graphs. We focus on recovering graph structures via
deleting irrelevant edges and adding missing edges, which has many applications
in real-world scenarios, for example, enhancing the quality of connections in a
co-authorship network. Mask-GVAE makes use of the robustness in low
eigenvectors of graph Laplacian against random noise and decomposes the input
graph into several stable clusters. It then harnesses the huge computations by
decoding probabilistic smoothed subgraphs in a variational manner. On a wide
variety of benchmarks, Mask-GVAE outperforms competing approaches by a
significant margin on PSNR and WL similarity.
