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Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training data.
Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 1902
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Graphmix: Regularized training of graph neural networks for semi-supervised learning
Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang · 1909
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Stochastic blockmodels: First steps
Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Diffusion kernels on graphs and other discrete structures
Risi Imre Kondor and John Lafferty · 2002
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Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
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Adversarial attacks and defenses on graphs: A review, a tool and empirical studies
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, and Jiliang Tang · 2003
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The imbalanced training sample problem: Under or over sampling?
Ricardo Barandela, Rosa M Valdovinos, J Salvador Sánchez, and Francesc J Ferri · 2004
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Semi-supervised learning with graphs
Xiaojin Zhu · 2005
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Graphcrop: Subgraph cropping for graph classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi · 2009
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Modeling information diffusion in networks with unobserved links
Quang Duong, Michael P Wellman, and Satinder Singh · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Spotting suspicious link behavior with fbox: An adversarial perspective
Neil Shah, Alex Beutel, Brian Gallagher, and Christos Faloutsos · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Efficient algorithms for public-private social networks
Flavio Chierichetti, Alessandro Epasto, Ravi Kumar, Silvio Lattanzi, and Vahab Mirrokni · 2015
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The (in) stability of functional brain network measures across thresholds
Kathleen A Garrison, Dustin Scheinost, Emily S Finn, Xilin Shen, and R Todd Constable · 2015
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Counterfactual evaluation and learning for search, recommendation and ad placement
Thorsten Joachims and Adith Swaminathan · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Network structure inference, a survey: Motivations, methods, and applications
Ivan Brugere, Brian Gallagher, and Tanya Y Berger-Wolf · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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False information on web and social media: A survey
Srijan Kumar and Neil Shah · 2018
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Can adversarial network attack be defended?
Jinyin Chen, Yangyang Wu, Xiang Lin, and Qi Xuan · 2019
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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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Adversarial training methods for network embedding
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, and Dan Wang · 2019
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Batch virtual adversarial training for graph convolutional networks
Zhijie Deng, Yinpeng Dong, and Jun Zhu · 2019
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Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
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Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
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Semi-supervised learning with graph learning-convolutional networks
Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo · 2019
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Diffusion improves graph learning
Augmentation-free self-supervised learning on graphs
Namkyeong Lee, Junseok Lee, and Chanyoung Park · 2021
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Local augmentation for graph neural networks
Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, and Dinghao Wu · 2021
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Learning to drop: Robust graph neural network via topological denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang · 2021
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A unified view on graph neural networks as graph signal denoising
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah · 2021
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Discrete graph structure learning for forecasting multiple time series
Chao Shang, Jie Chen, and Jinbo Bi · 2021
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Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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Automatically learning data augmentation policies for dialogue tasks
Tong Niu and Mohit Bansal · 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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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Graphdefense: Towards robust graph convolutional networks, 2019
Xiaoyun Wang, Xuanqing Liu, and Cho-Jui Hsieh · 2019
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Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou · 2019
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Rui Song, Fausto Giunchiglia, Ke Zhao, and Hao Xu · 2021
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Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning
Indro Spinelli, Simone Scardapane, Amir Hussain, and Aurelio Uncini · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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Augmentations in graph contrastive learning: Current methodological flaws & towards better practices
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, and Danai Koutra · 2021
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Discovering invariant rationales for graph neural networks
Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua · 2021
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Semi-supervised and self-supervised classification with multi-view graph neural networks
Jinliang Yuan, Hualei Yu, Meng Cao, Ming Xu, Junyuan Xie, and Chongjun Wang · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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From canonical correlation analysis to self-supervised graph neural networks
Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S Yu · 2021
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Randall Balestriero and Yann LeCun · 2022
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Graph barlow twins: A self-supervised representation learning framework for graphs
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Regularizing graph neural networks via consistency-diversity graph augmentations
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Learning causally invariant representations for out-of-distribution generalization on graphs
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Towards robust graph neural networks for noisy graphs with sparse labels
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang · 2022
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Dropmessage: Unifying random dropping for graph neural networks
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Graph neural networks: Adversarial robustness
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G-mixup: Graph data augmentation for graph classification
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Learning graph augmentations to learn graph representations
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Robust optimization as data augmentation for large-scale graphs
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Fair node representation learning via adaptive data augmentation
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Automated data augmentations for graph classification
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Interpretable and generalizable graph learning via stochastic attention mechanism
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Are defenses for graph neural networks robust?
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Link prediction with non-contrastive learning
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Graph structure learning with variational information bottleneck
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Large-scale representation learning on graphs via bootstrapping
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Understanding over-squashing and bottlenecks on graphs via curvature
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Analyzing data-centric properties for graph contrastive learning
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Self-supervised learning of graph neural networks: A unified review
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Data-augmented counterfactual learning for bundle recommendation
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