  Recent studies have indicated that Graph Convolutional Networks (GCNs) act as
a \emph{low pass} filter in spectral domain and encode smoothed node
representations. In this paper, we consider their opposite, namely Graph
Deconvolutional Networks (GDNs) that reconstruct graph signals from smoothed
node representations. We motivate the design of Graph Deconvolutional Networks
via a combination of inverse filters in spectral domain and de-noising layers
in wavelet domain, as the inverse operation results in a \emph{high pass}
filter and may amplify the noise. Based on the proposed GDN, we further propose
a graph autoencoder framework that first encodes smoothed graph representations
with GCN and then decodes accurate graph signals with GDN. We demonstrate the
effectiveness of the proposed method on several tasks including unsupervised
graph-level representation , social recommendation and graph generation
