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Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures.
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Harmonic analysis on semigroups
C. Berg, J. P. R. Christensen, and P. Ressel · 1984
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Convolution kernels on discrete structures
D. Haussler · 1999
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The Earth Mover’s Distance as a metric for image retrieval
Y. Rubner, C. Tomasi, and L. J. Guibas · 2000
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The kernel trick for distances
B. Schölkopf · 2001
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Learning with kernels: support vector machines, regularization, optimization, and beyond
B. Schölkopf and A. J. Smola · 2002
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Marginalized kernels between labeled graphs
H. Kashima, K. Tsuda, and A. Inokuchi · 2003
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Learning with distance substitution kernels
B. Haasdonk and C. Bahlmann · 2004
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Learning with non-positive kernels
C. S. Ong, X. Mary, S. Canu, and A. J. Smola · 2004
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Shortest-path kernels on graphs
K. M. Borgwardt and H.-P. Kriegel · 2005
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Protein function prediction via graph kernels
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel · 2005
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Optimal assignment kernels for attributed molecular graphs
H. Fröhlich, J. K. Wegner, F. Sieker, and A. Zell · 2005
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A theory of learning with similarity functions
M.-F. Balcan, A. Blum, and N. Srebro · 2008
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The optimal assignment kernel is not positive definite
J.-P. Vert · 2008
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Optimal transport: old and new , volume 338
C. Villani · 2008
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Fast subtree kernels on graphs
N. Shervashidze and K. M. Borgwardt · 2009
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Graph kernels
S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt · 2010
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Optimal transport and curvature
A. Figalli and C. Villani · 2011
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Wasserstein barycenter and its application to texture mixing
J. Rabin, G. Peyré, J. Delon, and M. Bernot · 2011
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Weisfeiler-Lehman graph kernels
N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt · 2011
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Subgraph matching kernels for attributed graphs
N. Kriege and P. Mutzel · 2012
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Barycenters in Alexandrov spaces of curvature bounded below
O. Shin-Ichi · 2012
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Metric spaces of non-positive curvature
Sliced Wasserstein kernels for probability distributions
S. Kolouri, Y. Zou, and G. K. Rohde · 2016
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On valid optimal assignment kernels and applications to graph classification
N. M. Kriege, P.-L. Giscard, and R. C. Wilson · 2016
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Faster kernels for graphs with continuous attributes via hashing
C. Morris, N. M. Kriege, K. Kersting, and P. Mutzel · 2016
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Propagation kernels: efficient graph kernels from propagated information
M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting · 2016
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Near-linear time approximation algorithms for optimal transport via sinkhorn iteration
J. Altschuler, J. Weed, and P. Rigollet · 2017
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M. R. Bridson and A. Häfliger · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
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Scalable kernels for graphs with continuous attributes
A. Feragen, N. Kasenburg, J. Petersen, M. de Bruijne, and K. Borgwardt · 2013
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Fréchet means for distributions of persistence diagrams
K. Turner, Y. Mileyko, S. Mukherjee, and J. Harer · 2014
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Geodesic exponential kernels: When curvature and linearity conflict
A. Feragen, F. Lauze, and S. Hauberg · 2015
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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POT: Python Optimal Transport library, 2017
R. Flamary and N. Courty · 2017
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On the definiteness of Earth Mover’s Distance and its relation to set intersection
A. Gardner, C. A. Duncan, J. Kanno, and R. R. Selmic · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Learning in reproducing kernel kreın spaces
D. Oglic and T. Gärtner · 2018
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graphkernels: R and python packages for graph comparison
M. Sugiyama, M. E. Ghisu, F. Llinares-López, and K. Borgwardt · 2018
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Combining neural networks with personalized pagerank for classification on graphs
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
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Computational optimal transport
G. Peyré, M. Cuturi, et al · 2019
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A persistent Weisfeiler–Lehman procedure for graph classification
B. Rieck, C. Bock, and K. Borgwardt · 2019
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Gromov–Wasserstein learning for graph matching and node embedding
H. Xu, D. Luo, H. Zha, and L. C. Duke · 2019
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