Towards Variational Generation of Small Graphs

Martin Simonovsky, Nikos Komodakis

Feb 09, 2018 (modified: Feb 09, 2018) ICLR 2018 Workshop Submission readers: everyone
  • Abstract: In this paper we propose a generative model for graphs formulated as a variational autoencoder. We sidestep hurdles associated with linearization of graphs by having the decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. We evaluate on the challenging task of molecule generation.
  • TL;DR: We present an autoencoder for graphs.
  • Keywords: graph, generative model, autoencoder

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