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Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for downstream tasks with unlabeled data, and it has recently become an active research area.
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Fracking deep convolutional image descriptors
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Online batch selection for faster training of neural networks
Ilya Loshchilov and Frank Hutter · 2015
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Ryan A. Rossi and Nesreen K. Ahmed · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 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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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Variational graph auto-encoders
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Training region-based object detectors with online hard example mining
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Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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Active learning for graph embedding
Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Overcoming catastrophic forgetting in neural networks
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graph2vec: Learning distributed representations of graphs
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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MGAE: Marginalized graph autoencoder for graph clustering
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang · 2017
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Active learning for graph embedding
Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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Large scale fine-grained categorization and domain-specific transfer learning
Yin Cui, Yang Song, Chen Sun, Andrew Howard, and Serge J. Belongie · 2018
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Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 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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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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When does self-supervision help graph convolutional networks
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 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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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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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec · 2020
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Human information processing in complex networks
Christopher W Lynn, Lia Papadopoulos, Ari E Kahn, and Danielle S Bassett · 2020
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GCC: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Are large-scale datasets necessary for self-supervised pre-training?
Alaaeldin El-Nouby, Gautier Izacard, Hugo Touvron, Ivan Laptev, Hervé Jegou, and Edouard Grave · 2021
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Pairwise half-graph discrimination: A simple graph-level self-supervised strategy for pre-training graph neural networks
Pengyong Li, Jun Wang, Ziliang Li, Yixuan Qiao, Xianggen Liu, Fei Ma, Peng Gao, Seng Song, and Guotong Xie · 2021
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Learning to pre-train graph neural networks
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi · 2021
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MoCL: Data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou · 2021
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Infogcl: Information-aware graph contrastive learning
Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang · 2021
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Contrastive self-supervised learning for graph classification
Jiaqi Zeng and Pengtao Xie · 2021
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ALG: fast and accurate active learning framework for graph convolutional networks
Wentao Zhang, Yu Shen, Yang Li, Lei Chen, Zhi Yang, and Bin Cui · 2021
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Motif-based graph self-supervised learning for molecular property prediction
Zaixin Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
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An empirical study of graph contrastive learning
Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Conditional contrastive learning for improving fairness in self-supervised learning
Martin Q Ma, Yao-Hung Hubert Tsai, Paul Pu Liang, Han Zhao, Kun Zhang, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2021
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Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Exploring the limits of large scale pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2022
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Efficient conditional pre-training for transfer learning
Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, and Stefano Ermon · 2022
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G-mixup: Graph data augmentation for graph classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu · 2022
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Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Augmentation-free self-supervised learning on graphs
Namkyeong Lee, Junseok Lee, and Chanyoung Park · 2022
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Analyzing data-centric properties for graph contrastive learning
Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, and Jayaraman Thiagarajan · 2022
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Unsupervised adversarially robust representation learning on graphs
Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu, and Yizhou Sun · 2022
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Exploring the limits of large scale pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2022
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Let invariant rationale discovery inspire graph contrastive learning
Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, and Tat-Seng Chua · 2022
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A survey of uncertainty in deep neural networks
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al · 2023
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Data-centric graph learning: A survey
Cheng Yang, Deyu Bo, Jixi Liu, Yufei Peng, Boyu Chen, Haoran Dai, Ao Sun, Yue Yu, Yixin Xiao, Qi Zhang, et al · 2023
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Data-centric ai: Perspectives and challenges
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, and Xia Hu · 2023
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Mole-bert: Rethinking pre-training graph neural networks for molecules
Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z Li · 2023
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