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International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 412–422Cite as

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11139))

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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Acknowledgments

We thank Shell Xu Hu for discussions on variational methods, Shinjae Yoo for project motivation, and anonymous reviewers for their comments.

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Authors and Affiliations

  1. Imagine & LIGM, Université Paris Est & École des Ponts, Champs sur Marne, France

    Martin Simonovsky & Nikos Komodakis

Authors
  1. Martin Simonovsky
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  2. Nikos Komodakis
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Corresponding author

Correspondence to Martin Simonovsky .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Simonovsky, M., Komodakis, N. (2018). GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_41

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  • DOI: https://doi.org/10.1007/978-3-030-01418-6_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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