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Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation.
Enhanced protein domain discovery by using language modeling techniques from speech recognition
Lachlan Coin, Alex Bateman, and Richard Durbin · 2003
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Network science
Albert-László Barabási · 2013
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 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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Deep modeling of social relations for recommendation
Wenqi Fan, Qing Li, and Min Cheng · 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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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Graphgan: Graph representation learning with generative adversarial nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
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Deep adversarial social recommendation
Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, and Qing Li · 2019
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Deep social collaborative filtering
Wenqi Fan, Yao Ma, Dawei Yin, Jianping Wang, Jiliang Tang, and Qing Li · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Learning representations of graph data–a survey
Mital Kinderkhedia · 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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Learning graph topological features via gan
Weiyi Liu, Pin-Yu Chen, Fucai Yu, Toyotaro Suzumura, and Guangmin Hu · 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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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Relaxing bijectivity constraints with continuously indexed normalising flows
Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet · 2020
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Epidemic graph convolutional network
Tyler Derr, Yao Ma, Wenqi Fan, Xiaorui Liu, Charu Aggarwal, and Jiliang Tang · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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A graph neural network framework for social recommendations
Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin · 2020
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Deep adversarial canonical correlation analysis
Wenqi Fan, Yao Ma, Han Xu, Xiaorui Liu, Jianping Wang, Qing Li, and Jiliang Tang · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon JD Prince, and Marcus A Brubaker · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
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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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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, and Junzhou Huang · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Global-and-local aware data generation for the class imbalance problem
Wentao Wang, Suhang Wang, Wenqi Fan, Zitao Liu, and Jiliang Tang · 2020
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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Accurate learning of graph representations with graph multiset pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang · 2021
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al · 2021
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Mdm: Molecular diffusion model for 3d molecule generation
Lei Huang, Hengtong Zhang, Tingyang Xu, and Ka-Chun Wong · 2022
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Equivariant 3d-conditional diffusion models for molecular linker design
Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, and Bruno Correia · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Regina Barzilay, and Tommi S. 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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Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G Willcocks · 2021
Cited alongside, same era.
Deep graph generators: A survey
Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah, and Hamid R Rabiee · 2021
Cited alongside, same era.
Attacking black-box recommendations via copying cross-domain user profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jianping Wang, Jiliang Tang, and Qing Li · 2021
Cited alongside, same era.
Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Octavian Ganea, Lagnajit Pattanaik, Connor Coley, Regina Barzilay, Klavs Jensen, William Green, and Tommi Jaakkola · 2021
Cited alongside, same era.
Adversarial attacks and defenses on graphs
Wei Jin, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, and Jiliang Tang · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
Cited alongside, same era.
Predicting molecular conformation via dynamic graph score matching
Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang · 2021
Cited alongside, same era.
Proteinsgm: Score-based generative modeling for de novo protein design
Jin Sub Lee and Philip M Kim · 2022
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Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2022
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Diffusion-lm improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto · 2022
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Diffbp: Generative diffusion of 3d molecules for target protein binding
Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, and Stan Z Li · 2022
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Trustworthy ai: A computational perspective
Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil Jain, and Jiliang Tang · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 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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End-to-end protein-ligand complex structure generation with diffusion-based generative models
Shuya Nakata, Yoshiharu Mori, and Shigenori Tanaka · 2022
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Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions
Leslie O’Bray, Max Horn, Bastian Rieck, and Karsten Borgwardt · 2022
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Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F Miller III, and Anima Anandkumar · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 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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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2022
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Protein structure generation via folding diffusion
Kevin E Wu, Kevin K Yang, Rianne van den Berg, James Y Zou, Alex X Lu, and Ava P Amini · 2022
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and qiang liu · 2022
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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Densepure: Understanding diffusion models towards adversarial robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, and Dawn Song · 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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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, Bin Cui, and Ming-Hsuan Yang · 2022
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Improving molecule properties through 2-stage vae
Chenghui Zhou and Barnabas Poczos · 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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3d equivariant diffusion for target-aware molecule generation and affinity prediction
Anonymous · 2023
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Autoregressive diffusion model for graph generation
Anonymous · 2023
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Pocket-specific 3d molecule generation by fragment-based autoregressive diffusion models
Anonymous · 2023
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Score-based graph generative modeling with self-guided latent diffusion
Anonymous · 2023
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Fairly adaptive negative sampling for recommendations
Xiao Chen, Wenqi Fan, Jingfan Chen, Haochen Liu, Zitao Liu, Zhaoxiang Zhang, and Qing Li · 2023
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Jointly attacking graph neural network and its explanations
Wenqi Fan, Han Xu, Wei Jin, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, and Charu Aggarwal · 2023
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Adversarial attacks for black-box recommender systems via copying transferable cross-domain user profiles
Wenqi Fan, Xiangyu Zhao, Qing LI, Tyler Derr, Yao Ma, Hui Liu, Jianping Wang, and Jiliang Tang · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces
Manon Réau, Nicolas Renaud, Li C Xue, and Alexandre MJJ Bonvin · 2023
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