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...

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Bibliographic Details
Published in:Neural networks 2021-10, Vol.142, p.1-19
Main Authors: Salha, Guillaume, Hennequin, Romain, Remy, Jean-Baptiste, Moussallam, Manuel, Vazirgiannis, Michalis
Format: Article
Language:English
Subjects:
Computer Science
Graph autoencoders
Graph convolutional networks
Graph variational autoencoders
Link prediction
Node clustering
Scalability
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2021.04.015