Fetching the paper…
Reading the bibliography…
We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner.
Latent space approaches to social network analysis
P. D. Hoff, A. E. Raftery, and M. S. Handcock · 2002
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
X. Zhu, Z. Ghahramani, and J. D. Lafferty · 2003
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Earlier work this paper cites.
Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
Earlier work this paper cites.
Structural deep network embedding
D. Wang, P. Cui, and W. Zhu · 2016
Cited alongside, same era.
Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
A. Bojchevski and S. Günnemann · 2017
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Cited alongside, same era.
On sampling strategies for neural network-based collaborative filtering
T. Chen, Y. Sun, Y. Shi, and L. Hong · 2017
Cited alongside, same era.
Learning graph representations with embedding propagation
A. G. Duran and M. Niepert · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
Later among the works it cites.
Gaan: Gated attention networks for learning on large and spatiotemporal graphs
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung · 2018
Later among the works it cites.
Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
Later among the works it cites.
Hyperbolic graph convolutional neural networks
I. Chami, Z. Ying, C. Ré, and J. Leskovec · 2019
Later among the works it cites.
Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
M. Zitnik and J. Leskovec · 2017
Cited alongside, same era.
Fastgcn: fast learning with graph convolutional networks via importance sampling
J. Chen, T. Ma, and C. Xiao · 2018
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio · 2018
Cited alongside, same era.
Formal limitations on the measurement of mutual information
D. McAllester and K. Stratos · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Break the ceiling: Stronger multi-scale deep graph convolutional networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2019
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
I. Misra and L. van der Maaten · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Later among the works it cites.
Y. Tian, D. Krishnan, and P. Isola · 2019
Later among the works it cites.
On mutual information maximization for representation learning
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic · 2019
Later among the works it cites.
Local aggregation for unsupervised learning of visual embeddings
C. Zhuang, A. L. Zhai, and D. Yamins · 2019
Later among the works it cites.
A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Closest in time.