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In this paper we cast neural networks defined on graphs as message-passing neural networks (MPNNs) in order to study the distinguishing power of different classes of such models.
A comprehensive survey on graph neural networks
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Pinet: A permutation invariant graph neural network for graph classification
Meltzer, P., Mallea, M. D. G., and Bentley, P. J. (2019) · 1905
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Local and global properties in networks of processors (extended abstract)
Angluin, D. (1980) · 1980
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Describing graphs: A first-order approach to graph canonization
Immerman, N. and Lander, E. (1990) · 1990
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An optimal lower bound on the number of variables for graph identifications
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A survey on the expressive power of graph neural networks
Sato, R. (2020) · 2003
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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) · 2004
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Algebraic number theory
Jarvis, F. (2014) · 2014
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Positivity problems for low-order linear recurrence sequences
Ouaknine, J. and Worrell, J. (2014) · 2014
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Graphs identified by logics with counting
Kiefer, S., Schweitzer, P., and Selman, E. (2015) · 2015
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Graph isomorphism, color refinement, and compactness
Arvind, V., Köbler, J., Rattan, G., and Verbitsky, O. (2017) · 2017
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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) · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J. (2017) · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R., and Smola, A. J. (2017) · 2017
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edGNN: a simple and powerful GNN for directed labeled graphs
Jaume, G., Nguyen, A., Martínez, M. R., Thiran, J., and Gabrani, M. (2019) · 2019
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What graph neural networks cannot learn: depth vs width
Loukas, A. (2019) · 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) · 2019
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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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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
Cited alongside, same era.