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Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC).
Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair and Geoffrey E Hinton · 2010
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Revisiting Semi-supervised Learning with Graph Embeddings
Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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Inductive Representation Learning on Large Graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised Classification with Graph Convolutional Networks
Thomas N Kipf and Max Welling · 2017
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Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity
Eliav Buchnik and Edith Cohen · 2018
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Deeper Insights into Graph Convolutional Networks for Semi-supervised Learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Feastnet: Feature-steered Graph Convolutions for 3d Shape Analysis
Nitika Verma, Edmond Boyer, and Jakob Verbeek · 2018
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Remixmatch: Semi-supervised Learning with Distribution Alignment and Augmentation Anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2019
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Graph Neural Networks with Convolutional Arma Filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi · 2019
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Weisfeiler and Leman go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Heterogeneous Deep Graph Infomax
Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, and Jiawei Zhang · 2019
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Fisher-Bures Adversary Graph Convolutional Networks
Ke Sun, Piotr Koniusz, and Jeff Wang · 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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GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang · 2019
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PairNorm: Tackling Oversmoothing in GNNs
Lingxiao Zhao and Leman Akoglu · 2019
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Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun · 2020
NodeAug: Semi-Supervised Node Classification with Data Augmentation
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi · 2020
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Understanding Negative Sampling in Graph Representation Learning
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, 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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GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna · 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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Hypergraph Convolution and Hypergraph Attention
Song Bai, Feihu Zhang, and Philip HS Torr · 2021
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MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
Jiaao Chen, Zichao Yang, and Diyi Yang · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
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Graph random neural networks for semi-supervised learning on graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang · 2020
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Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
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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Probing Negative Sampling Strategies to Learn Graph Representations via Unsupervised Contrastive Learning
Shiyi Chen, Ziao Wang, Xinni Zhang, Xiaofeng Zhang, and Dan Peng · 2021
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Towards Robust Graph Contrastive Learning
Nikola Jovanovic, Zhao Meng, Lukas Faber, and Roger Wattenhofer · 2021
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Self-supervised Graph Neural Networks without Explicit Negative Sampling
Zekarias T. Kefato and Sarunas Girdzijauskas · 2021
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Improving Graph Representation Learning by Contrastive Regularization
Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, and James Cheng · 2021
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CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Weizhu Chen, and Jiawei Han · 2021
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Contrastive Learning with Hard Negative Samples
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2021
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Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Sheng Wan, Shirui Pan, Jian Yang, and Chen Gong · 2021
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Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization
Chenguang Wang and Ziwen Liu · 2021
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Unsupervised Data Augmentation for Consistency Training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le · 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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