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Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets.
Towards a unified min-max framework for adversarial exploration and robustness
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A simple framework for contrastive learning of visual representations
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Distinguishing enzyme structures from non-enzymes without alignments
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On graph kernels: Hardness results and efficient alternatives
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Shortest-path kernels on graphs
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Weisfeiler-lehman graph kernels
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Subgraph matching kernels for attributed graphs
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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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
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Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
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Deep graph kernels
Yanardag, P. and Vishwana, S · 2015
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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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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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
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Neural message passing for quantum chemistry
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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graph2vec: Learning distributed representations of graphs
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., and Jaiswal, S · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Sub2vec: Feature learning for subgraphs
Adhikari, B., Zhang, Y., Ramakrishnan, N., and Prakash, B. A · 2018
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Deep clustering for unsupervised learning of visual features
Caron, M., Bojanowski, P., Joulin, A., and Douze, M · 2018
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
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Flag: Adversarial data augmentation for graph neural networks
Kong, K., Li, G., Ding, M., Wu, Z., Zhu, C., Ghanem, B., Taylor, G., and Goldstein, T · 2020
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Differentiable automatic data augmentation
Li, Y., Hu, G., Wang, Y., Hospedales, T., Robertson, N. M., and Yang, Y · 2020
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Mernyei, P. and Cangea, C · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Adversarially regularized graph autoencoder for graph embedding
Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., and Zhang, C · 2018
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 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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Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Kool, W., Van Hoof, H., and Welling, M · 2019
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Graph representation learning via graphical mutual information maximization
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., and Huang, J · 2020
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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., and Tang, J · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2020
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Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P · 2020
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Infograph: Unsupervised graph-level representation learning via mutual information maximization
Sun, F.-Y., Hoffman, J., Verma, V., and Tang, J · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2020
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Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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A survey on data augmentation approaches for nlp
Feng, S., Gangal, V. P., Wei, J., Vosoughi, S., Chandar, S., Mitamura, T., and Hovy, E · 2021
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Interpreting graph neural networks for {nlp} with differentiable edge masking
Schlichtkrull, M. S., Cao, N. D., and Titov, I · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Suresh, S., Li, P., Hao, C., and Neville, J · 2021
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Bootstrapped representation learning on graphs
Thakoor, S., Tallec, C., Azar, M. G., Munos, R., Veličković, P., and Valko, M · 2021
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Self-supervised learning of graph neural networks: A unified review
Xie, Y., Xu, Z., Zhang, J., Wang, Z., and Ji, S · 2021
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Graph contrastive learning automated
You, Y., Chen, T., Shen, Y., and Wang, Z · 2021
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Data augmentation for graph neural networks
Zhao, T., Liu, Y., Neves, L., Woodford, O., Jiang, M., and Shah, N · 2021
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Graph contrastive learning with adaptive augmentation
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2021
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Cross-domain few-shot graph classification
Hassani, K · 2022
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