Graph Autoencoders with Deconvolutional Networks
This work addresses the problem of reconstructing accurate graph signals from smoothed representations, which is relevant for researchers working with graph neural networks and their applications.
This paper introduces Graph Deconvolutional Networks (GDNs) to reconstruct graph signals from smoothed node representations, acting as a high-pass filter. They then propose a graph autoencoder that uses GCNs for encoding and GDNs for decoding, demonstrating its effectiveness on tasks like unsupervised graph-level representation, social recommendation, and graph generation.
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