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Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis.
Reducing the bandwidth of sparse symmetric matrices
Elizabeth Cuthill and James McKee · 1969
Earlier work this paper cites.
The bandwidth problem for graphs and matrices—a survey
Phyllis Z Chinn, Jarmila Chvátalová, Alexander K Dewdney, and Norman E Gibbs · 1982
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Polyominoes: Puzzles, patterns, problems, and packings
Elizabeth Senger · 1997
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Head-driven statistical models for natural language parsing
Michael Collins · 2003
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Brenda, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
Earlier work this paper cites.
Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer · 2018
Earlier work this paper cites.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
Earlier work this paper cites.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 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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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
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Graph normalizing flows
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky · 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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Pytorch: An imperative style, high-performance deep learning library
Graphgen-redux: A fast and lightweight recurrent model for labeled graph generation
Davide Bacciu and Marco Podda · 2021
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Graphebm: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
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Graphdf: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
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Spanning tree-based graph generation for molecules
Sungsoo Ahn, Binghong Chen, Tianzhe Wang, and Le Song · 2022
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Data-efficient graph grammar learning for molecular generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik · 2022
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Scalable deep generative modeling for sparse graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans · 2020
Cited alongside, same era.
Graphgen: a scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
Cited alongside, same era.
Mol-cyclegan: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł · 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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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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Fast graph generative model via spectral diffusion
Tianze Luo, Zhanfeng Mo, and Sinno Jialin Pan · 2022
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Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, and Roger Wattenhofer · 2022
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Improving graph generation by restricting graph bandwidth
Nathaniel Lee Diamant, Alex M Tseng, Kangway V Chuang, Tommaso Biancalani, and Gabriele Scalia · 2023
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Graph generation with destination-driven diffusion mixture
Jaehyeong Jo, Dongki Kim, and Sung Ju Hwang · 2023
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Autoregressive diffusion model for graph generation, 2023
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, and Chao Zhang · 2023
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Swingnn: Rethinking permutation invariance in diffusion models for graph generation
Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, and Lele Wang · 2023
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Graph generation with $k^2$-trees
Yunhui Jang, Dongwoo Kim, and Sungsoo Ahn · 2024
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