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This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.
Spectral graph theory , volume 92
Fan RK Chung and Fan Chung Graham · 1997
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Rdkit: Open-source cheminformatics
Greg Landrum et al · 2006
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, and Oleksandr Polozov · 2019
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
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Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Real-time traffic speed estimation with graph convolutional generative autoencoder
James Jian Qiao Yu and Jiatao Gu · 2019
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
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Compressed graph representation for scalable molecular graph generation
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, and Seokho Kang · 2020
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Permutation invariant graph generation via score-based generative modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
Top-n: Equivariant set and graph generation without exchangeability
Clement Vignac and Pascal Frossard · 2021
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein · 2022
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Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks
Davide Buffelli, Pietro Liò, and Fabio Vandin · 2022
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Diffusion models for graphs benefit from discrete state spaces
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, and Roger Wattenhofer · 2022
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Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
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Psi4 1.4: Open-source software for high-throughput quantum chemistry
Daniel GA Smith, Lori A Burns, Andrew C Simmonett, Robert M Parrish, Matthew C Schieber, Raimondas Galvelis, Peter Kraus, Holger Kruse, Roberto Di Remigio, Asem Alenaizan, et al · 2020
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Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, and Pascal Frossard · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Directional graph networks
Dominique Beaini, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, and Pietro Liò · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Equivariant diffusion for molecule generation in 3d
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models
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Learning to extend molecular scaffolds with structural motifs
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
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A score-based geometric model for molecular dynamics simulations
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Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Diffsound: Discrete diffusion model for text-to-sound generation
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, and Dong Yu · 2022
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A survey on deep graph generation: Methods and applications
Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu · 2022
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