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Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design.
On the distinction between the conditional probability and the joint probability approaches in the specification of nearest-neighbour systems
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A general method for numerically simulating the stochastic time evolution of coupled chemical reactions
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Exact stochastic simulation of coupled chemical reactions
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Adventures in stochastic processes
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Approximate accelerated stochastic simulation of chemically reacting systems
Daniel T Gillespie · 2001
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A modified next reaction method for simulating chemical systems with time dependent propensities and delays
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Interpretation and generalization of score matching
Siwei Lyu · 2009
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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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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Automatic chemical design using a data-driven continuous representation of molecules
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GANS for sequences of discrete elements with the gumbel-softmax distribution
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2018
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville · 2018
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Molecular sets (MOSES): A benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamín Sánchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Sergey I. Nikolenko, Alán Aspuru-Guzik, and Alex Zhavoronkov · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
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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 L. Hamilton, and Jure Leskovec · 2018
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Guacamol: Benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin H. S. Segler, and Alain C. Vaucher · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. 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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Misc-gan: A multi-scale generative model for graphs
Dawei Zhou, Lecheng Zheng, Jiejun Xu, and Jingrui He · 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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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Velickovic · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Gg-gan: A geometric graph generative adversarial network, 2021
Igor Krawczuk, Pedro Abranches, Andreas Loukas, and Volkan Cevher · 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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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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A data-driven graph generative model for temporal interaction networks
Dawei Zhou, Lecheng Zheng, Jiawei Han, and Jingrui He · 2020
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Structured denoising diffusion models in discrete state-spaces
Top-n: Equivariant set and graph generation without exchangeability
Clément Vignac and Pascal Frossard · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Graph sanitation with application to node classification
Zhe Xu, Boxin Du, and Hanghang Tong · 2022
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui · 2022
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Equivariant energy-guided sde for inverse molecular design
Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, and Jun Zhu · 2023
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Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Predicting molecular conformation via dynamic graph score matching
Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang · 2021
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Graph networks for molecular design
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, and Esben Jannik Bjerrum · 2021
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Graph networks for molecular design
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, and Esben Jannik Bjerrum · 2021
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A graph VAE and graph transformer approach to generating molecular graphs
Joshua Mitton, Hans M. Senn, Klaas Wynne, and Roderick Murray-Smith · 2021
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On the connection between MPNN and graph transformer
Chen Cai, Truong Son Hy, Rose Yu, and Yusu Wang · 2023
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Efficient and degree-guided graph generation via discrete diffusion modeling
Xiaohui Chen, Jiaxing He, Xu Han, and Li-Ping Liu · 2023
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Natural and artificial dynamics in gnns: A tutorial
Dongqi Fu, Zhe Xu, Hanghang Tong, and Jingrui He · 2023
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MDM: molecular diffusion model for 3d molecule generation
Lei Huang, Hengtong Zhang, Tingyang Xu, and Ka-Chun Wong · 2023
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Graph generation with destination-predicting diffusion mixture
Jaehyeong Jo, Dongki Kim, and Sung Ju Hwang · 2023
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Understanding diffusion objectives as the ELBO with simple data augmentation
Diederik P. Kingma and Ruiqi Gao · 2023
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Autoregressive diffusion model for graph generation
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, and Chao Zhang · 2023
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Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2023
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Generative diffusion models on graphs: Methods and applications
Chengyi Liu, Wenqi Fan, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, and Qing Li · 2023
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Data-centric learning from unlabeled graphs with diffusion model
Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang · 2023
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Graph neural bandits
Yunzhe Qi, Yikun Ban, and Jingrui He · 2023
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Reconstructing graph diffusion history from a single snapshot
Ruizhong Qiu, Dingsu Wang, Lei Ying, H. Vincent Poor, Yifang Zhang, and Hanghang Tong · 2023
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Score-based continuous-time discrete diffusion models
Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, and Hanjun Dai · 2023
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Digress: Discrete denoising diffusion for graph generation
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
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Networked time series imputation via position-aware graph enhanced variational autoencoders
Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew Margenot, and Hanghang Tong · 2023
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Guided diffusion for inverse molecular design
Tomer Weiss, Eduardo Mayo Yanes, Sabyasachi Chakraborty, Luca Cosmo, Alex M Bronstein, and Renana Gershoni-Poranne · 2023
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Geometric latent diffusion models for 3d molecule generation
Minkai Xu, Alexander S Powers, Ron O Dror, Stefano Ermon, and Jure Leskovec · 2023
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Kernel ridge regression-based graph dataset distillation
Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, and Hanghang Tong · 2023
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Neural contextual bandits for personalized recommendation
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Inverse molecular design with multi-conditional diffusion guidance
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Class-imbalanced graph learning without class rebalancing
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Graph mixup on approximate gromov-wasserstein geodesics
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