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Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors.
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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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K. M. Borgwardt and H. P. Kriegel · 2005
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P. F. Felzenszwalb and D. P. Huttenlocher · 2005
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Z. W. Tu, X. R. Chen, A. L. Yuille, and S. C. Zhu · 2005
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Comparison of descriptor spaces for chemical compound retrieval and classification
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The graph neural network model
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Gated graph sequence neural networks
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Interaction networks for learning about objects, relations and physics
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F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Efficient graphlet kernels for large graph comparison
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Weisfeiler-Lehman graph kernels
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Molecular graph convolutions: moving beyond fingerprints
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On valid optimal assignment kernels and applications to graph classification
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Propagation kernels: efficient graph kernels from propagated information
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Learning convolutional neural networks for graphs
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Semi-supervised classification with graph convolutional networks
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Quantum-chemical insights from deep tensor neural networks
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Harmonic networks: Deep translation and rotation equivariance
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