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We present a hierarchical neural message passing architecture for learning on molecular graphs.
A reduction of a graph to a canonical form and an algebra arising during this reduction
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Feature trees: A new molecular similarity measure based on tree matching
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Open Graph Benchmark: Datasets for machine learning on graphs
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GraphRNN: Generating realistic graphs with deep auto-regressive models
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On representing chemical environments
Bartók, A. P., Kondor, R., and Csányi, G · 2013
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Convolutional networks on graphs for learning molecular fingerprints
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Adam: A method for stochastic optimization
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Semi-supervised classification with graph convolutional networks
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Grammar variational autoencoder
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Lin, T. Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
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Predicting molecular properties with covariant compositional networks
Hy, T. S., Trivedi, S., Pan, H., Anderson, B., and Kondor, R · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Graph U-Nets
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Learning multimodal graph-to-graph translation for molecule optimization
Jin, W., Yang, K., Barzilay, R., and Jaakkola, T · 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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Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
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Cross-lingual knowledge graph alignment via graph convolutional networks
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Hierarchical graph representation learning with differentiable pooling
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PhysNet: A neural network for predicting energies, force, dipole moments, and partial charges
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How powerful are graph neural networks?
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Benchmarking graph neural networks
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Deep graph matching consensus
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Directional message passing for molecular graphs
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What graph neural networks cannot learn: Depth vs width
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