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Graph kernels are historically the most widely-used technique for graph classification tasks.
Explainability techniques for graph convolutional networks
Baldassarre, F.; and Azizpour, H. 2019 · 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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Provably powerful graph networks
Maron, H.; Ben-Hamu, H.; Serviansky, H.; and Lipman, Y. 2019 · 1905
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Approximation ratios of graph neural networks for combinatorial problems
Sato, R.; Yamada, M.; and Kashima, H. 2019 · 1905
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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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A reduction of a graph to a canonical form and an algebra arising during this reduction
Leman, A.; and Weisfeiler, B. 1968 · 1968
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Convolution kernels on discrete structures
Haussler, D. 1999 · 1999
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Geom-gcn: Geometric graph convolutional networks
Pei, H.; Wei, B.; Chang, K. C.-C.; Lei, Y.; and Yang, B. 2020 · 2002
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Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D.; and Doig, A. J. 2003 · 2003
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On graph kernels: Hardness results and efficient alternatives
Gärtner, T.; Flach, P.; and Wrobel, S. 2003 · 2003
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Marginalized kernels between labeled graphs
Kashima, H.; Tsuda, K.; and Inokuchi, A. 2003 · 2003
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Directional message passing for molecular graphs
Klicpera, J.; Groß, J.; and Günnemann, S. 2020 · 2003
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BRENDA, the enzyme database: updates and major new developments
Schomburg, I.; Chang, A.; Ebeling, C.; Gremse, M.; Heldt, C.; Huhn, G.; and Schomburg, D. 2004 · 2004
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Shortest-path kernels on graphs
Borgwardt, K. M.; and Kriegel, H.-P. 2005 · 2005
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Protein function prediction via graph kernels
Borgwardt, K. M.; Ong, C. S.; Schönauer, S.; Vishwanathan, S.; Smola, A. J.; and Kriegel, H.-P. 2005 · 2005
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Fast computation of graph kernels
Vishwanathan, S.; Borgwardt, K. M.; Schraudolph, N. N.; et al. 2006 · 2006
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Collective classification in network data
Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008 · 2008
Cited alongside, same era.
Efficient graphlet kernels for large graph comparison
Shervashidze, N.; Vishwanathan, S.; Petri, T.; Mehlhorn, K.; and Borgwardt, K. 2009 · 2009
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Weisfeiler-lehman graph kernels
Shervashidze, N.; Schweitzer, P.; Van Leeuwen, E. J.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
Cited alongside, same era.
Mairal, J.; Koniusz, P.; Harchaoui, Z.; and Schmid, C. 2014 · 2014
Cited alongside, same era.
Deep graph kernels
Yanardag, P.; and Vishwanathan, S. 2015 · 2015
Cited alongside, same era.
Benchmark Data Sets for Graph Kernels
Kersting, K.; Kriege, N. M.; Morris, C.; Mutzel, P.; and Neumann, M. 2016 · 2016
Kernel graph convolutional neural networks
Nikolentzos, G.; Meladianos, P.; Tixier, A. J.-P.; Skianis, K.; and Vazirgiannis, M. 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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Link prediction based on graph neural networks
Zhang, M.; and Chen, Y. 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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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S.; Perozzi, B.; Kapoor, A.; Alipourfard, N.; Lerman, K.; Harutyunyan, H.; Ver Steeg, G.; and Galstyan, A. 2019 · 2019
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Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
Cited alongside, same era.
End-to-end kernel learning with supervised convolutional kernel networks
Mairal, J. 2016 · 2016
Cited alongside, same era.
Propagation kernels: efficient graph kernels from propagated information
Neumann, M.; Garnett, R.; Bauckhage, C.; and Kersting, K. 2016 · 2016
Cited alongside, same era.
Supervised community detection with line graph neural networks
Chen, Z.; Li, X.; and Bruna, J. 2017 · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
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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Explainability methods for graph convolutional neural networks
Pope, P. E.; Kolouri, S.; Rostami, M.; Martin, C. E.; and Hoffmann, H. 2019 · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Ying, R.; Bourgeois, D.; You, J.; Zitnik, M.; and Leskovec, J. 2019 · 2019
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Convolutional kernel networks for graph-structured data
Chen, D.; Jacob, L.; and Mairal, J. 2020 · 2020
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Simple and deep graph convolutional networks
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; and Li, Y. 2020 · 2020
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A survey on graph kernels
Kriege, N. M.; Johansson, F. D.; and Morris, C. 2020 · 2020
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Graph Homomorphism Convolution
Nguyen, H.; and Maehara, T. 2020 · 2020
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Random Walk Graph Neural Networks
Nikolentzos, G.; and Vazirgiannis, M. 2020 · 2020
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GraKeL: A Graph Kernel Library in Python
Siglidis, G.; Nikolentzos, G.; Limnios, S.; Giatsidis, C.; Skianis, K.; and Vazirgiannis, M. 2020 · 2020
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Multi-scale attributed node embedding
Rozemberczki, B.; Allen, C.; and Sarkar, R. 2021 · 2021
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Deriving neural architectures from sequence and graph kernels
Lei, T.; Jin, W.; Barzilay, R.; and Jaakkola, T. 2017 · 2033
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