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Graph property prediction tasks are important and numerous.
Gas separation properties of 6fda-based polyimide membranes with a polar group
Sang-Hee Park, Kwang-Je Kim, Won-Wook So, Sang-Jin Moon, and Soo-Bok Lee · 2003
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Scaffold hopping
Hans-Joachim Böhm, Alexander Flohr, and Martin Stahl · 2004
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Molecularly imprinted polymers: synthesis and characterisation
Peter AG Cormack and Amaia Zurutuza Elorza · 2004
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Polyinfo: Polymer database for polymeric materials design
Shingo Otsuka, Isao Kuwajima, Junko Hosoya, Yibin Xu, and Masayoshi Yamazaki · 2011
Earlier work this paper cites.
Polymer gas separation membrane database, 2012
A Thornton, L Robeson, B Freeman, and D Uhlmann · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Bioisosteres and scaffold hopping in medicinal chemistry
Nathan Brown · 2014
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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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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Visual representations: Defining properties and deep approximations
Stefano Soatto and Alessandro Chiuso · 2016
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Gan augmentation: Augmenting training data using generative adversarial networks
Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, and Daniel Rueckert · 2018
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Feature space transfer for data augmentation
Bo Liu, Xudong Wang, Mandar Dixit, Roland Kwitt, and Nuno Vasconcelos · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 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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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Understanding attention and generalization in graph neural networks
Boris Knyazev, Graham W Taylor, and Mohamed Amer · 2019
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 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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Jointly modelling uncertainty and diversity for active molecular property prediction
Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
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Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah · 2021
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The effects of regularization and data augmentation are class dependent
Randall Balestriero, Leon Bottou, and Yann LeCun · 2022
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Data augmentation for deep graph learning: A survey
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu · 2022
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G-mixup: Graph data augmentation for graph classification
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Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, and Xinchao Wang · 2019
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Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus · 2020
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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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Macromolecular design strategies toward tailoring free volume in glassy polymers for high performance gas separation membranes
Tanner Corrado and Ruilan Guo · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Quantifying the evaluation of heuristic methods for textual data augmentation
Omid Kashefi and Rebecca Hwa · 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
Cited alongside, same era.
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Uncertainty-aware pseudo-labeling for quantum calculations
Kexin Huang, Vishnu Sresht, Brajesh Rai, and Mykola Bordyuh · 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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Graph self-supervised learning with accurate discrepancy learning
Dongki Kim, Jinheon Baek, and Sung Ju Hwang · 2022
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Robust optimization as data augmentation for large-scale graphs
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2022
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Automated data augmentations for graph classification
Youzhi Luo, Michael McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino, Koji Maruhash, and Shuiwang Ji · 2022
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Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations
Ruimin Ma, Hanfeng Zhang, Jiaxin Xu, Luning Sun, Yoshihiro Hayashi, Ryo Yoshida, Junichiro Shiomi, Jian-xun Wang, and Tengfei Luo · 2022
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Does gnn pretraining help molecular representation?
Ruoxi Sun, Hanjun Dai, and Adams Wei Yu · 2022
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Analyzing data-centric properties for graph contrastive learning
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann, Danai Koutra, and Jayaraman J Thiagarajan · 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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Learning from counterfactual links for link prediction
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang · 2022
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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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Motif-aware attribute masking for molecular graph pre-training
Eric Inae, Gang Liu, and Meng Jiang · 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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Semi-supervised graph imbalanced regression
Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, and Meng Jiang · 2023
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Graph data augmentation for graph machine learning: A survey
Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günneman, Neil Shah, and Meng Jiang · 2023
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