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Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.
Zur theorie der orthogonalen funktionensysteme
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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Derivation and validation of toxicophores for mutagenicity prediction
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
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Comparison of descriptor spaces for chemical compound retrieval and classification
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
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Hammond, D. K., Vandergheynst, P., and Gribonval, R · 2011
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Subgraph matching kernels for attributed graphs
Kriege, N. and Mutzel, P · 2012
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Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R., and von Lilienfeld, O. A · 2012
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A linear time-complexity k-means algorithm using cluster shifting
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Representation of functions on big data: graphs and trees
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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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Hierarchical graph representation learning with differentiable pooling
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M · 2018
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Towards graph pooling by edge contraction
Diehl, F., Brunner, T., Le, M. T., and Knoll, A · 2019
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Fast graph representation learning with pytorch geometric
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
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Benchmark data sets for graph kernels, 2016
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Fast Haar transforms for graph neural networks
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A comprehensive survey on graph neural networks
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How powerful are graph neural networks?
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