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This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery.
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Grarep: Learning graph representations with global structural information
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
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Moleculenet: a benchmark for molecular machine learning
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Hierarchical graph representation learning with differentiable pooling
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BERT: pre-training of deep bidirectional transformers for language understanding
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Momentum contrast for unsupervised visual representation learning
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Pre-training graph neural networks
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Invariant information clustering for unsupervised image classification and segmentation
Ji, X., Vedaldi, A., and Henriques, J. F · 2019
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Sun, F., Hoffmann, J., and Tang, J · 2019
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Deep graph infomax
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How powerful are graph neural networks?
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A simple framework for contrastive learning of visual representations
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Contrastive multi-view representation learning on graphs
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Gpt-gnn: Generative pre-training of graph neural networks
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Prototypical contrastive learning of unsupervised representations
Li, J., Zhou, P., Xiong, C., Socher, R., and Hoi, S. C. H · 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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Self-supervised graph transformer on large-scale molecular data
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Learning to pre-train graph neural networks
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