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This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions.
A stochastic approximation method
Robbins, H. and Monro, S · 1951
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A reduction of a graph to a canonical form and an algebra arising during this reduction
Weisfeiler, B. and Lehman, A · 1968
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La prévision: ses lois logiques, ses sources subjectives
De Finetti, B · 1980
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Representations for partially exchangeable arrays of random variables
Aldous, D. J · 1981
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Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
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Backpropagation applied to handwritten zip code recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D · 1989
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An optimal lower bound on the number of variables for graph identification
Cai, J.-Y., Fürer, M., and Immerman, N · 1992
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates
Younes, L · 1999
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No unbiased estimator of the variance of k-fold cross-validation
Bengio, Y. and Grandvalet, Y · 2004
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On circulant graphs
Vilfred, V · 2004
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The convergence of contrastive divergences
Yuille, A. L · 2005
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Infinite latent feature models and the Indian buffet process
Ghahramani, Z. and Griffiths, T. L · 2006
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Graph limits and exchangeable random graphs
Diaconis, P. and Janson, S · 2008
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Maximum unbiased validation (muv) data sets for virtual screening based on pubchem bioactivity data
Rohrer, S. G. and Baumann, K · 2009
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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Wisfeiler-Lehman graph kernels
Shervashidze, N., Schweitzer, P., Leeuwen, E. J. v., Mehlhorn, K., and Borgwardt, K. M · 2011
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Stochastic gradient descent tricks
Bottou, L · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Learning invariant representations of molecules for atomization energy prediction
Montavon, G., Hansen, K., Fazli, S., Rupp, M., Biegler, F., Ziehe, A., Tkatchenko, A., Lilienfeld, A. V., and Müller, K.-R · 2012
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3D convolutional neural networks for human action recognition
Ji, S., Xu, W., Yang, M., and Yu, K · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Tractability through exchangeability: A new perspective on efficient probabilistic inference
Niepert, M. and Van den Broeck, G · 2014
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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
Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Deep models of interactions across sets
Hartford, J., Graham, D. R., Leyton-Brown, K., and Ravanbakhsh, S · 2018
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Graph classification using structural attention
Lee, J. B., Rossi, R., and Kong, X · 2018
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Kingma, D. P. and Ba, J. L · 2015
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Bayesian models of graphs, arrays and other exchangeable random structures
Orbanz, P. and Roy, D. M · 2015
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Diffusion-convolutional neural networks
Atwood, J. and Towsley, D · 2016
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs
Huang, R., Xia, M., Nguyen, D.-T., Zhao, T., Sakamuru, S., Zhao, J., Shahane, S. A., Rossoshek, A., and Simeonov, A · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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Subgraph pattern neural networks for high-order graph evolution prediction
Meng, C., Mouli, S. C., Ribeiro, B., and Neville, J · 2018
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Kernel graph convolutional neural networks
Nikolentzos, G., Meladianos, P., Tixier, A. J.-P., Skianis, K., and Vazirgiannis, M · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Sanchez-Lengeling, B. and Aspuru-Guzik, A · 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 pattern mining and learning through user-defined relations
Teixeira, C. H., Cotta, L., Ribeiro, B., and Meira, W · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 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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Representation Learning on Graphs with Jumping Knowledge Networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Probabilistic symmetry and invariant neural networks
Bloem-Reddy, B. and Teh, Y. W · 2019
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On the universality of invariant networks
Maron, H., Fetaya, E., Segol, N., 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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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
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Deep Learning for the Life Sciences
Ramsundar, B., Eastman, P., Leswing, K., Walters, P., and Pande, V · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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