FastGAE: Scalable graph autoencoders with stochastic subgraph decoding

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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper introduces FastGAE, a general framework to scale graph AE and VAE to large graphs, which significantly speeds up training and improves performance through an effective stochastic subgraph decoding scheme. FastGAE demonstrates effectiveness on various real-world graphs, outperforming existing approaches by a wide margin.