Fetching the paper…
Reading the bibliography…
Graph contrastive learning algorithms have demonstrated remarkable success in various applications such as node classification, link prediction, and graph clustering.
Modularity and community structure in networks
M. E. J. Newman · 2006
Earlier work this paper cites.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael Gutmann and Aapo Hyvärinen · 2012
Earlier work this paper cites.
A fast and simple algorithm for training neural probabilistic language models
Andriy Mnih and Yee Whye Teh · 2012
Earlier work this paper cites.
Distributed large-scale natural graph factorization
Amr Ahmed, Nino Shervashidze, Shravan M. Narayanamurthy, Vanja Josifovski, and Alexander J. Smola · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Convex Optimization
Stephen P. Boyd and Lieven Vandenberghe · 2014
Earlier work this paper cites.
Notes on noise contrastive estimation and negative sampling
Chris Dyer · 2014
Earlier work this paper cites.
Deepwalk: online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Robust multi-view spectral clustering via low-rank and sparse decomposition
Rongkai Xia, Yan Pan, Lei Du, and Jian Yin · 2014
Earlier work this paper cites.
Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
LINE: large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Earlier work this paper cites.
Network representation learning with rich text information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y. Chang · 2015
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
A vector-contraction inequality for rademacher complexities
Andreas Maurer · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton · 2017
Earlier work this paper cites.
struc2vec : Learning node representations from structural identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
Earlier work this paper cites.
Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, and Aaron C. Courville · 2018
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Earlier work this paper cites.
Harp: Hierarchical representation learning for networks
Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena · 2018
Earlier work this paper cites.
Curse of dimensionality
Lei Chen · 2018
Cited alongside, same era.
Semi-supervised learning on graphs with generative adversarial nets
Ming Ding, Jie Tang, and Jie Zhang · 2018
Cited alongside, same era.
Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 2018
Cited alongside, same era.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Self-labelling via simultaneous clustering and representation learning
Yuki M. Asano, Christian Rupprecht, and Andrea Vedaldi · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Later among the works it cites.
Graph representation learning via graphical mutual information maximization
Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang · 2020
Later among the works it cites.
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
Later among the works it cites.
What makes for good views for contrastive learning
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance-level discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Batch virtual adversarial training for graph convolutional networks
Zhijie Deng, Yinpeng Dong, and Jun Zhu · 2019
Cited alongside, same era.
Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
Cited alongside, same era.
Later among the works it cites.
Understanding negative sampling in graph representation learning
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Later among the works it cites.
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
Later among the works it cites.
Graphnorm: A principled approach to accelerating graph neural network training
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, and Liwei Wang · 2021
Closest in time.
Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
Closest in time.
Directed graph contrastive learning
Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang · 2021
Closest in time.
Rethinking graph regularization for graph neural networks
Han Yang, Kaili Ma, and James Cheng · 2021
Closest in time.
Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, and Jian Li · 2021
Closest in time.
Graph debiased contrastive learning with joint representation clustering
Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng · 2021
Closest in time.
Adaptive label smoothing to regularize large-scale graph training
Kaixiong Zhou, Ninghao Liu, Fan Yang, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu · 2021
Closest in time.
G-mixup: Graph data augmentation for graph classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu · 2022
Closest in time.
Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
Closest in time.
Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng · 2022
Closest in time.
Link prediction with non-contrastive learning
William Shiao, Zhichun Guo, Tong Zhao, Evangelos E. Papalexakis, Yozen Liu, and Neil Shah · 2022
Closest in time.
Graphmae2: A decoding-enhanced masked self-supervised graph learner
Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, and Jie Tang · 2023
Closest in time.
Seegera: Self-supervised semi-implicit graph variational auto-encoders with masking
Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, and Ming Gao · 2023
Closest in time.
Opencon: Open-world contrastive learning
Yiyou Sun and Yixuan Li · 2023
Closest in time.
S2GAE: self-supervised graph autoencoders are generalizable learners with graph masking
Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, and Xia Hu · 2023
Closest in time.
Graph neural networks designed for different graph types: A survey
Josephine Thomas, Alice Moallemy-Oureh, Silvia Beddar-Wiesing, and Clara Holzhüter · 2023
Closest in time.