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Graph neural networks (GNNs) have attracted much attention because of their excellent performance on tasks such as node classification.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2019b) · 1901
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Revisiting graph neural networks: All we have is low-pass filters
NT, H. and Maehara, T. (2019) · 1905
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Convolutional networks for images, speech, and time series
LeCun, Y. and Bengio, Y. (1995) · 1995
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., and Eliassi-Rad, T. (2008) · 2008
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Wavelets on graphs via spectral graph theory
Hammond, D. K., Vandergheynst, P., and Gribonval, R. (2011) · 2011
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Karush-kuhn-tucker conditions
Gordon, G. and Tibshirani, R. (2012) · 2012
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Discrete signal processing on graphs
Sandryhaila, A. and Moura, J. M. F. (2013) · 2013
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Discrete signal processing on graphs: Frequency analysis
Sandryhaila, A. and Moura, J. M. F. (2014) · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2015) · 2015
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Signal recovery on graphs: Variation minimization
Chen, S., Sandryhaila, A., Moura, J. M. F., and Kovacevic, J. (2015) · 2015
Cited alongside, same era.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y. (2015) · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E. (2015) · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016) · 2016
Cited alongside, same era.
Graph signal recovery via primal-dual algorithms for total variation minimization
Berger, P., Hannak, G., and Matz, G. (2017) · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J. (2017) · 2017
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V. F., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gülçehre, Ç., Song, H. F., Ballard, A. J., Gilmer, J., Dahl, G. E., Vaswani, A., Allen, K. R., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R. (2018) · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X. (2018) · 2018
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Attention-based graph neural network for semi-supervised learning
Thekumparampil, K. K., Wang, C., Oh, S., and Li, L. (2018) · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018) · 2018
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E. (2019) · 2019
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Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J. (2017) · 2017
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Simplifying graph convolutional networks
Wu, F., Jr., A. H. S., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. Q. (2019a) · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2019) · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Ying, Z., Bourgeois, D., You, J., Zitnik, M., and Leskovec, J. (2019) · 2019
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