  Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node
embedding methods, but suffer from scalability issues. In this paper, we
introduce FastGAE, a general framework to scale graph AE and VAE to large
graphs with millions of nodes and edges. Our strategy, based on an effective
stochastic subgraph decoding scheme, significantly speeds up the training of
graph AE and VAE while preserving or even improving performances. We
demonstrate the effectiveness of FastGAE on various real-world graphs,
outperforming the few existing approaches to scale graph AE and VAE by a wide
margin.
