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Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data.
On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z.; Villar, S.; Chen, L.; and Bruna, J. 2019b · 1905
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Du, S. S.; Hou, K.; Póczos, B.; Salakhutdinov, R.; Wang, R.; and Xu, K. 2019 · 1905
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Understanding attention and generalization in graph neural networks
Knyazev, B.; Taylor, G. W.; and Amer, M. R. 2019 · 1905
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Provably powerful graph networks
Maron, H.; Ben-Hamu, H.; Serviansky, H.; and Lipman, Y. 2019 · 1905
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Alchemy: A quantum chemistry dataset for benchmarking ai models
Chen, G.; Chen, P.; Hsieh, C.-Y.; Lee, C.-K.; Liao, B.; Liao, R.; Liu, W.; Qiu, J.; Sun, Q.; Tang, J.; et al. 2019a · 1906
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Understanding the representation power of graph neural networks in learning graph topology
Dehmamy, N.; Barabási, A.-L.; and Yu, R. 2019 · 1907
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Coloring graph neural networks for node disambiguation
Dasoulas, G.; Santos, L. D.; Scaman, K.; and Virmaux, A. 2019 · 1912
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A fair comparison of graph neural networks for graph classification
Errica, F.; Podda, M.; Bacciu, D.; and Micheli, A. 2019 · 1912
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Practical graph isomorphism
McKay, B. D.; et al. 1981 · 1981
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Can graph neural networks count substructures?
Chen, Z.; Chen, L.; Villar, S.; and Bruna, J. 2020 · 2002
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Random features strengthen graph neural networks
Sato, R.; Yamada, M.; and Kashima, H. 2020 · 2002
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Benchmarking graph neural networks
Dwivedi, V. P.; Joshi, C. K.; Laurent, T.; Bengio, Y.; and Bresson, X. 2020 · 2003
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Directional message passing for molecular graphs
Klicpera, J.; Groß, J.; and Günnemann, S. 2020 · 2003
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Principal neighbourhood aggregation for graph nets
Corso, G.; Cavalleri, L.; Beaini, D.; Liò, P.; and Veličković, P. 2020 · 2004
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Exploiting structure in symmetry detection for CNF
Darga, P. T.; Liffiton, M. H.; Sakallah, K. A.; and Markov, I. L. 2004 · 2004
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G.; Frasca, F.; Zafeiriou, S.; and Bronstein, M. M. 2020 · 2006
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Building powerful and equivariant graph neural networks with structural message-passing
Vignac, C.; Loukas, A.; and Frossard, P. 2020 · 2006
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Distance Encoding–Design Provably More Powerful GNNs for Structural Representation Learning
Li, P.; Wang, Y.; Wang, H.; and Leskovec, J. 2020 · 2009
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Neuralizing Efficient Higher-order Belief Propagation
Dupty, M. H.; and Lee, W. S. 2020 · 2010
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Conflict propagation and component recursion for canonical labeling
Junttila, T.; and Kaski, P. 2011 · 2011
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Weisfeiler-lehman graph kernels
Shervashidze, N.; Schweitzer, P.; Van Leeuwen, E. J.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
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Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Ruddigkeit, L.; Van Deursen, R.; Blum, L. C.; and Reymond, J.-L. 2012 · 2012
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Representation learning: A review and new perspectives
Bengio, Y.; Courville, A.; and Vincent, P. 2013 · 2013
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
Cited alongside, same era.
Practical graph isomorphism, II
How powerful are graph neural networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018 · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, R.; You, J.; Morris, C.; Ren, X.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
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An end-to-end deep learning architecture for graph classification
Zhang, M.; Cui, Z.; Neumann, M.; and Chen, Y. 2018 · 2018
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The logical expressiveness of graph neural networks
Barceló, P.; Kostylev, E. V.; Monet, M.; Pérez, J.; Reutter, J.; and Silva, J. P. 2019 · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 2019 · 2019
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McKay, B. D.; and Piperno, A. 2014 · 2014
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R.; Dral, P. O.; Rupp, M.; and Von Lilienfeld, O. A. 2014 · 2014
Cited alongside, same era.
Order matters: Sequence to sequence for sets
Vinyals, O.; Bengio, S.; and Kudlur, M. 2015 · 2015
Cited alongside, same era.
Deep graph kernels
Yanardag, P.; and Vishwanathan, S. 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.
Benchmark data sets for graph kernels, 2016
Kersting, K.; Kriege, N. M.; Morris, C.; Mutzel, P.; and Neumann, M. 2016 · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Self-attention graph pooling
Lee, J.; Lee, I.; and Kang, J. 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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Relational pooling for graph representations
Murphy, R.; Srinivasan, B.; Rao, V.; and Ribeiro, B. 2019 · 2019
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On the Equivalence between Positional Node Embeddings and Structural Graph Representations
Srinivasan, B.; and Ribeiro, B. 2019 · 2019
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PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
Unke, O. T.; and Meuwly, M. 2019 · 2019
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On Weisfeiler-Leman invariance: Subgraph counts and related graph properties
Arvind, V.; Fuhlbrück, F.; Köbler, J.; and Verbitsky, O. 2020 · 2020
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Power and limits of the Weisfeiler-Leman algorithm
Kiefer, S.; Immerman, N.; Schweitzer, P.; and Grohe, M. 2020 · 2020
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Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Morris, C.; Rattan, G.; and Mutzel, P. 2020 · 2020
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Random Walk Graph Neural Networks
Nikolentzos, G.; and Vazirgiannis, M. 2020 · 2020
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A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Philip, S. Y. 2020 · 2020
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Directional graph networks
Beani, D.; Passaro, S.; Létourneau, V.; Hamilton, W.; Corso, G.; and Liò, P. 2021 · 2021
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Graph u-nets
Gao, H.; and Ji, S. 2019 · 2092
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