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Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations.
Self-Organization in a Perceptual Network
Ralph Linsker · 1988
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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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Protein Function Prediction via Graph Kernels
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, and Hans-Peter Kriegel · 2005
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Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
Nikil Wale and George Karypis · 2006
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Fast Random Walk with Restart and Its Applications
Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan · 2006
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Noise-Contrastive Estimation: a New Estimation Principle for Unnormalized Statistical Models
Michael Gutmann and Aapo Hyvärinen · 2010
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Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Michael Gutmann and Aapo Hyvärinen · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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An Experimental Investigation of Kernels on Graphs for Collaborative Recommendation and Semisupervised Classification
François Fouss, Kevin Françoisse, Luh Yen, Alain Pirotte, and Marco Saerens · 2012
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Learning Word Embeddings Efficiently with Noise-Contrastive Estimation
Andriy Mnih and Koray Kavukcuoglu · 2013
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Dropout: A Simple Way to Prevent Neural Networks From Overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2014
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Deep Graph Kernels
Pinar Yanardag and S. V. N. Vishwanathan · 2015
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Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep Learning and the Information Bottleneck Principle
Naftali Tishby and Noga Zaslavsky · 2015
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Colorization as a Proxy Task for Visual Understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 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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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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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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Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Learning Deep Representations by Mutual Information Estimation and Maximization
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R. Devon Hjelm, and William Buchwalter · 2019
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Deep Graph Infomax
Petar Veličković, 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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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Diffusion Improves Graph Learning
On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 2020
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Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Less Can Be More in Contrastive Learning
Jovana Mitrovic, Brian McWilliams, and Melanie Rey · 2020
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Are All Negatives Created Equal in Contrastive Instance Discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, and Ari S Morcos · 2020
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Debiased Contrastive Learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka · 2020
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Hard Negative Mixing for Contrastive Learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus · 2020
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Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, and George Tucker · 2019
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Contrastive Self-Supervised Learning for Commonsense Reasoning
Tassilo Klein and Moin Nabi · 2020
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CERT: Contrastive Self-supervised Learning for Language Understanding
Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, and Pengtao Xie · 2020
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i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee · 2020
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Spatiotemporal Contrastive Video Representation Learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge J. Belongie, and Yin Cui · 2021
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SimCSE: Simple Contrastive Learning of Sentence Embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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Graph Self-Supervised Learning: A Survey
Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S. Yu · 2021
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Bootstrapped Representation Learning on Graphs
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, and Michal Valko · 2021
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Graph Barlow Twins: A Self-Supervised Representation Learning Framework for Graphs
Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V. Chawla · 2021
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Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs
Costas Mavromatis and George Karypis · 2021
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When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, and Serge Belongie · 2021
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Contrasting Contrastive Self-Supervised Representation Learning Pipelines
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, and Roozbeh Mottaghi · 2021
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Understanding the Behaviour of Contrastive Loss
Feng Wang and Huaping Liu · 2021
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Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra · 2021
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OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Deep Generative Models for Spatial Networks
Xiaojie Guo, Yuanqi Du, and Liang Zhao · 2021
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GraphGT: Machine Learning Datasets for Deep Graph Generation and Transformation
Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, and Liang Zhao · 2021
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Simple Spectral Graph Convolution
Hao Zhu and Piotr Koniusz · 2021
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Understanding Self-Supervised Learning Dynamics without Contrastive Pairs
Yuandong Tian, Xinlei Chen, and Surya Ganguli · 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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Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning
Yao-Hung Hubert Tsai, Shaojie Bai, Louis-Philippe Morency, and Ruslan R. Salakhutdinov · 2021
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VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Contrastive Learning with Hard Negative Samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2021
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Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2080
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