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We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs.
Graph convolutional networks using heat kernel for semi-supervised learning
Xu, B., Shen, H., Cao, Q., Cen, K., and Cheng, X · 1934
Earlier work this paper cites.
Asymptotic evaluation of certain markov process expectations for large time
Donsker, M. D. and Varadhan, S. S · 1975
Earlier work this paper cites.
Self-organization in a perceptual network
Linsker, R · 1988
Earlier work this paper cites.
The pagerank citation ranking: Bringing order to the web
Page, L., Brin, S., Motwani, R., and Winograd, T · 1999
Earlier work this paper cites.
Diffusion kernels on graphs and other discrete structures
Kondor, R. I. and Lafferty, J · 2002
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
Gärtner, T., Flach, P., and Wrobel, S · 2003
Earlier work this paper cites.
Link-based classification
Lu, Q. and Getoor, L · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Zhu, X., Ghahramani, Z., and Lafferty, J. D · 2003
Earlier work this paper cites.
Shortest-path kernels on graphs
Borgwardt, K. M. and Kriegel, H.-P · 2005
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Belkin, M., Niyogi, P., and Sindhwani, V · 2006
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
Earlier work this paper cites.
Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Leeuwen, E. J. v., Mehlhorn, K., and Borgwardt, K. M · 2011
Earlier work this paper cites.
Subgraph matching kernels for attributed graphs
Kriege, N. and Mutzel, P · 2012
Earlier work this paper cites.
Deep learning via semi-supervised embedding
Weston, J., Ratle, F., Mobahi, H., and Collobert, R · 2012
Earlier work this paper cites.
Adam: Amethod for stochastic optimization
Kingma, D. P. and Ba, J. L · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
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Line: Large-scale information network embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q · 2015
Cited alongside, same era.
Deep graph kernels
Yanardag, P. and Vishwana, S · 2015
Cited alongside, same era.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
Cited alongside, same era.
Verse: Versatile graph embeddings from similarity measures
Tsitsulin, A., Mottin, D., Karras, P., and Müller, E · 2018
Later among the works it cites.
Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Later among the works it cites.
Pixel2mesh: Generating 3d mesh models from single rgb images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., and Jiang, Y.-G · 2018
Later among the works it cites.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Later among the works it cites.
Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
Later among the works it cites.
Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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The multiscale laplacian graph kernel
Kondor, R. and Pan, H · 2016
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On valid optimal assignment kernels and applications to graph classification
Kriege, N. M., Giscard, P.-L., and Wilson, R · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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Learning graph representations with embedding propagation
Garcia Duran, A. and Niepert, M · 2017
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Unsupervised multi-task feature learning on point clouds
Hassani, K. and Haley, M · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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Data representation and learning with graph diffusion-embedding networks
Jiang, B., Lin, D., Tang, J., and Luo, B · 2019
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Graph matching networks for learning the similarity of graph structured objects
Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P · 2019
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Symmetric graph convolutional autoencoder for unsupervised graph representation learning
Park, J., Lee, M., Chang, H. J., Lee, K., and Choi, J. Y · 2019
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Tian, Y., Krishnan, D., and Isola, P · 2019
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Deep graph infomax
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
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Relational graph representation learning for open-domain question answering
Vivona, S. and Hassani, K · 2019
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Neural graph evolution: Automatic robot design
Wang, T., Zhou, Y., Fidler, S., and Ba, J · 2019
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Position-aware graph neural networks
You, J., Ying, R., and Leskovec, J · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Memory-based graph networks
Khasahmadi, A., Hassani, K., Moradi, P., Lee, L., and Morris, Q · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Sun, F.-Y., Hoffman, J., Verma, V., and Tang, J · 2020
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Deep learning on graphs: A survey
Zhang, Z., Cui, P., and Zhu, W · 2020
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