A Systematic Survey on Deep Generative Models for Graph Generation-Reference-Cited by-同舟云学术

A Systematic Survey on Deep Generative Models for Graph Generation

Author:

Guo Xiaojie¹^ORCID,Zhao Liang²^ORCID

Affiliation:

1. IBM T. J. Watson Research Center, Yorktown Height, NY

2. Department of Computer Science, Emory University, Atlanta, GA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference133 articles.

1. MolGAN: An implicit generative model for small molecular graphs;de cao;Proc Int Conf Mach Learn Workshop,2018

2. Likelihood-free inference and generation of molecular graphs;pölsterl,2019

3. DiPol-GAN: Generating molecular graphs adversarially with relational differentiable pooling;guarino,2017

4. A two-step graph convolutional decoder for molecule generation;bresson,2019

5. Grammar variational autoencoder;kusner;Proc 34th Int Conf Mach Learn,2017

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