论文标题
使用反向倾斜网络的图形自动编码器
Graph Autoencoders with Deconvolutional Networks
论文作者
论文摘要
最近的研究表明，图形卷积网络（GCN）在光谱域中充当\ emph {low pass}滤波器，并编码平滑的节点表示。在本文中，我们考虑了它们相反的，即图形反向倾斜网络（GDN），这些网络（GDN）从平滑节点表示中重建图形信号。我们通过在光谱域中的反过滤器和小波域中的降压层的组合来激发图形反向倾斜网络的设计，因为反操作导致\ emph {高通}滤波器并可能扩大噪声。基于提出的GDN，我们进一步提出了一个图形自动编码器框架，该框架首先用GCN编码平滑的图表表示，然后用GDN解码准确的图形信号。我们证明了提出方法对几个任务的有效性，包括无监督的图形表示，社会建议和图形生成
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
