Graph Deconvolutional Generation

• URL: http://arxiv.org/abs/2002.07087v1
• Date: Fri, 14 Feb 2020 04:37:14 GMT
• Title: Graph Deconvolutional Generation
• Authors: Daniel Flam-Shepherd, Tony Wu and Alan Aspuru-Guzik
• Abstract summary: We focus on the modern equivalent of the Erdos-Renyi random graph model: the graph variational autoencoder (GVAE) GVAE has difficulty matching the training distribution and relies on an expensive graph matching procedure. We improve this class of models by building a message passing neural network into GVAE's encoder and decoder.
• Score: 3.5138314002170192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the Erdos-Renyi random graph model: the graph variational autoencoder (GVAE). This model assumes edges and nodes are independent in order to generate entire graphs at a time using a multi-layer perceptron decoder. As a result of these assumptions, GVAE has difficulty matching the training distribution and relies on an expensive graph matching procedure. We improve this class of models by building a message passing neural network into GVAE's encoder and decoder. We demonstrate our model on the specific task of generating small organic molecules

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