Simple and Effective Graph Autoencoders with One-Hop Linear Models

Understand

Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.

• Graph AE, VAE and most of their extensions rely on multi-layer graph convolutional networks (GCN) encoders to learn vector space representations of nodes.
• In this paper, we show that GCN encoders are actually unnecessarily complex for many applications.
• We propose to replace them by significantly simpler and more interpretable linear models w.r.t.