FastGAE: Scalable graph autoencoders with stochastic subgraph decoding

Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy, Manuel Moussallam, Michalis Vazirgiannis

Abstract: 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.