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An Empirical Study of Graph Contrastive Learning

An Empirical Study of Graph Contrastive Learning

2 September 2021
Yanqiao Zhu
Yichen Xu
Qiang Liu
Shu Wu
ArXivPDFHTML

Papers citing "An Empirical Study of Graph Contrastive Learning"

28 / 78 papers shown
Title
Measuring and Relieving the Over-smoothing Problem for Graph Neural
  Networks from the Topological View
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
Xu Sun
75
1,096
0
07 Sep 2019
On Mutual Information Maximization for Representation Learning
On Mutual Information Maximization for Representation Learning
Michael Tschannen
Josip Djolonga
Paul Kishan Rubenstein
Sylvain Gelly
Mario Lucic
SSL
149
490
0
31 Jul 2019
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation
  Learning via Mutual Information Maximization
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun
Jordan Hoffmann
Vikas Verma
Jian Tang
SSL
143
852
0
31 Jul 2019
Contrastive Multiview Coding
Contrastive Multiview Coding
Yonglong Tian
Dilip Krishnan
Phillip Isola
SSL
147
2,385
0
13 Jun 2019
Learning Representations by Maximizing Mutual Information Across Views
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman
R. Devon Hjelm
William Buchwalter
SSL
179
1,463
0
03 Jun 2019
Strategies for Pre-training Graph Neural Networks
Strategies for Pre-training Graph Neural Networks
Weihua Hu
Bowen Liu
Joseph Gomes
Marinka Zitnik
Percy Liang
Vijay S. Pande
J. Leskovec
SSL
AI4CE
91
1,377
0
29 May 2019
Data-Efficient Image Recognition with Contrastive Predictive Coding
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff
A. Srinivas
J. Fauw
Ali Razavi
Carl Doersch
S. M. Ali Eslami
Aaron van den Oord
SSL
101
1,422
0
22 May 2019
On Variational Bounds of Mutual Information
On Variational Bounds of Mutual Information
Ben Poole
Sherjil Ozair
Aaron van den Oord
Alexander A. Alemi
George Tucker
SSL
74
802
0
16 May 2019
Fast Graph Representation Learning with PyTorch Geometric
Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey
J. E. Lenssen
3DH
GNN
3DPC
183
4,303
0
06 Mar 2019
Pitfalls of Graph Neural Network Evaluation
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur
Maximilian Mumme
Aleksandar Bojchevski
Stephan Günnemann
GNN
128
1,345
0
14 Nov 2018
How Powerful are Graph Neural Networks?
How Powerful are Graph Neural Networks?
Keyulu Xu
Weihua Hu
J. Leskovec
Stefanie Jegelka
GNN
199
7,554
0
01 Oct 2018
Deep Graph Infomax
Deep Graph Infomax
Petar Velickovic
W. Fedus
William L. Hamilton
Pietro Lio
Yoshua Bengio
R. Devon Hjelm
GNN
112
2,368
0
27 Sep 2018
Learning deep representations by mutual information estimation and
  maximization
Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm
A. Fedorov
Samuel Lavoie-Marchildon
Karan Grewal
Phil Bachman
Adam Trischler
Yoshua Bengio
SSL
DRL
271
2,649
0
20 Aug 2018
Representation Learning with Contrastive Predictive Coding
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord
Yazhe Li
Oriol Vinyals
DRL
SSL
260
10,152
0
10 Jul 2018
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying
Ruining He
Kaifeng Chen
Pong Eksombatchai
William L. Hamilton
J. Leskovec
GNN
BDL
222
3,513
0
06 Jun 2018
Unsupervised Feature Learning via Non-Parametric Instance-level
  Discrimination
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination
Zhirong Wu
Yuanjun Xiong
Stella X. Yu
Dahua Lin
SSL
160
3,437
0
05 May 2018
Unsupervised Representation Learning by Predicting Image Rotations
Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris
Praveer Singh
N. Komodakis
OOD
SSL
DRL
217
3,272
0
21 Mar 2018
Deeper Insights into Graph Convolutional Networks for Semi-Supervised
  Learning
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li
Zhichao Han
Xiao-Ming Wu
GNN
SSL
176
2,796
0
22 Jan 2018
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang
Jia Wang
Jialin Wang
Miao Zhao
Weinan Zhang
Fuzheng Zhang
Xing Xie
Minyi Guo
GNN
GAN
75
623
0
22 Nov 2017
Graph Attention Networks
Graph Attention Networks
Petar Velickovic
Guillem Cucurull
Arantxa Casanova
Adriana Romero
Pietro Lio
Yoshua Bengio
GNN
388
19,991
0
30 Oct 2017
Colorization as a Proxy Task for Visual Understanding
Colorization as a Proxy Task for Visual Understanding
Gustav Larsson
Michael Maire
Gregory Shakhnarovich
SSL
140
494
0
11 Mar 2017
MoleculeNet: A Benchmark for Molecular Machine Learning
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu
Bharath Ramsundar
Evan N. Feinberg
Joseph Gomes
C. Geniesse
Aneesh S. Pappu
K. Leswing
Vijay S. Pande
OOD
307
1,808
0
02 Mar 2017
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf
Max Welling
GNN
SSL
540
28,901
0
09 Sep 2016
f-GAN: Training Generative Neural Samplers using Variational Divergence
  Minimization
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin
Botond Cseke
Ryota Tomioka
GAN
111
1,648
0
02 Jun 2016
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff
Dmitry Kalenichenko
James Philbin
3DH
316
13,079
0
12 Mar 2015
Deep Learning and the Information Bottleneck Principle
Deep Learning and the Information Bottleneck Principle
Naftali Tishby
Noga Zaslavsky
DRL
152
1,570
0
09 Mar 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
391
43,154
0
11 Feb 2015
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
371
43,511
0
17 Sep 2014
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