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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Martin Simonovsky 1, 2, 3 Nikos Komodakis 1, 2, 3 
3 imagine [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech
Abstract : Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of molecule generation.
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Submitted on : Wednesday, January 23, 2019 - 9:35:24 AM
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  • HAL Id : hal-01990381, version 1



Martin Simonovsky, Nikos Komodakis. GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders. 27th International Conference on Artificial Neural Networks (ICANN), Oct 2018, Rhodes, Greece. ⟨hal-01990381⟩



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