Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Fast CNN Pruning via Redundancy-Aware Training Read chapter Read first chapter

2018 | OriginalPaper | Chapter

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Authors : Martin Simonovsky, Nikos Komodakis

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
next chapter Generation of Reference Trajectories for Safe Trajectory Planning
Literature
1.
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: NIPS, pp. 1171–1179 (2015)
2.
Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Józefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: CoNLL, pp. 10–21 (2016)
3.
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)CrossRef
4.
Cho, M., Sun, J., Duchenne, O., Ponce, J.: Finding matches in a haystack: a max-pooling strategy for graph matching in the presence of outliers. In: CVPR, pp. 2091–2098 (2014)
5.
Gómez-Bombarelli, R., et al.: Automatic chemical design using a data-driven continuous representation of molecules. CoRR abs/1610.02415 (2016)
6.
Irwin, J.J., Sterling, T., Mysinger, M.M., Bolstad, E.S., Coleman, R.G.: ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52(7), 1757–1768 (2012)CrossRef
7.
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-Softmax. CoRR abs/1611.01144 (2016)
8.
Johnson, D.D.: Learning graphical state transitions. In: ICLR (2017)
9.
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. CoRR abs/1312.6114 (2013)
10.
Kusner, M.J., Hernández-Lobato, J.M.: GANS for sequences of discrete elements with the Gumbel-Softmax distribution. CoRR abs/1611.04051 (2016)
11.
Kusner, M.J., Paige, B., Hernández-Lobato, J.M.: Grammar variational autoencoder. In: ICML, pp. 1945–1954 (2017)
12.
13.
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. CoRR abs/1511.05493 (2015)
14.
Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H.: Molecular de novo design through deep reinforcement learning. CoRR abs/1704.07555 (2017)
15.
Ramakrishnan, R., Dral, P.O., Rupp, M., von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014)CrossRef
16.
Segler, M.H.S., Kogej, T., Tyrchan, C., Waller, M.P.: Generating focussed molecule libraries for drug discovery with recurrent neural networks. CoRR abs/1701.01329 (2017)
17.
Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: CVPR (2017)
18.
Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: NIPS, pp. 3483–3491 (2015)
19.
Stewart, R., Andriluka, M., Ng, A.Y.: End-to-end people detection in crowded scenes. In: CVPR, pp. 2325–2333 (2016)
20.
Theis, L., van den Oord, A., Bethge, M.: A note on the evaluation of generative models. CoRR abs/1511.01844 (2015)
21.
22.
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRef
23.
Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: CVPR (2017)
24.
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: AAAI (2017)
Metadata
Title
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Authors
Martin Simonovsky
Nikos Komodakis
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

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

Premium Partner

    Image Credits