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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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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