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Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data.
Spline-Fitting with a Genetic Algorithm: A Method for Developing ClassificationS tructure-Activity Relationships
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Smallest-last Ordering and Clustering and Graph Coloring Algorithms
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Network Structure and Minimum Degree
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Structure-Activity Relationship of Mutagenic Aromatic and Heteroaromatic Nitro Compounds. Correlation with Molecular Orbital Energies and Hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C. (1991) · 1991
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Color Indexing
Swain, M. J., and Ballard, D. H. (1991) · 1991
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A Training Algorithm for Optimal Margin Classifiers
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992) · 1992
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Supervised Neural Networks for the Classification of Structures
Sperduti, A., and Starita, A. (1997) · 1997
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Classification on Pairwise Proximity Data
Graepel, T., Herbrich, R., Bollmann-Sdorra, P., and Obermayer, K. (1999) · 1999
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Convolution kernels on discrete structures
Haussler, D. (1999) · 1999
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Convolution Kernels for Natural Language
Collins, M., and Duffy, N. (2001) · 2001
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Statistical evaluation of the Predictive Toxicology Challenge 2000–2001
Toivonen, H., Srinivasan, A., King, R., Kramer, S., and Helma, C. (2003) · 2001
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Using the Nyström Method to Speed Up Kernel Machines
Williams, C. K., and Seeger, M. (2001) · 2001
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Diffusion Kernels on Graphs and Other Discrete Input Spaces
Kondor, R. I., and Lafferty, J. (2002) · 2002
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., and Smola, A. J. (2002) · 2002
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A tree kernel to analyse phylogenetic profiles
Vert, J.-P. (2002) · 2002
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Histogram intersection kernel for image classification
Barla, A., Odone, F., and Verri, A. (2003) · 2003
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Distinguishing Enzyme Structures from Non-enzymes Without Alignments
Dobson, P., and Doig, A. (2003) · 2003
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A Survey of Kernels for Structured Data
Gärtner, T. (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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Expressivity versus Efficiency of Graph Kernels
Ramon, J., and Gärtner, T. (2003) · 2003
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Kernels and Regularization on Graphs
Smola, A. J., and Kondor, R. (2003) · 2003
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Fast kernels for string and tree matching
Smola, A. J., and Vishwanathan, S. (2003) · 2003
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Inequalities for the l 1 l_{1} deviation of the empirical distribution
Weissman, T., Ordentlich, E., Seroussi, G., Verdu, S., and Weinberger, M. J. (2003) · 2003
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Kernel Methods for Relation Extraction
Zelenko, D., Aone, C., and Richardella, A. (2003) · 2003
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Thirty years of graph matching in pattern recognition
Conte, D., Foggia, P., Sansone, C., and Vento, M. (2004) · 2004
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Dependency Tree Kernels for Relation Extraction
Culotta, A., and Sorensen, J. (2004) · 2004
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Kernel k-means, Spectral Clustering and Normalized Cuts
Dhillon, I. S., Guan, Y., and Kulis, B. (2004) · 2004
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Cyclic Pattern Kernels for Predictive Graph Mining
Horváth, T., Gärtner, T., and Wrobel, S. (2004) · 2004
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Extensions of marginalized graph kernels
Mahé, P., Ueda, N., Akutsu, T., Perret, J.-L., and Vert, J.-P. (2004) · 2004
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TextRank: Bringing Order into Texts
Mihalcea, R., and Tarau, P. (2004) · 2004
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A Study on Convolution Kernels for Shallow Semantic Parsing
Moschitti, A. (2004) · 2004
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Kernel Methods for Pattern Analysis
Shawe-Taylor, J., and Cristianini, N. (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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A Shortest Path Dependency Kernel for Relation Extraction
Bunescu, R., and Mooney, R. (2005) · 2005
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Optimal Assignment Kernels For Attributed Molecular Graphs
Fröhlich, H., Wegner, J. K., Sieker, F., and Zell, A. (2005) · 2005
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Graph Kernels for Molecular Structure-Activity Relationship Analysis with SupportVector Machines
Mahé, P., Ueda, N., Akutsu, T., Perret, J.-L., and Vert, J.-P. (2005) · 2005
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Graph Kernels for Chemical Informatics
Ralaivola, L., Swamidass, S. J., Saigo, H., and Baldi, P. (2005) · 2005
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Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity
Swamidass, S. J., Chen, J., Bruand, J., Phung, P., Ralaivola, L., and Baldi, P. (2005) · 2005
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Large scale networks fingerprinting and visualization using the k-core decomposition
Alvarez-Hamelin, I., Dall’Asta, L., Barrat, A., and Vespignani, A. (2006) · 2006
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Graph kernels and gaussian processes for relational reinforcement learning
Driessens, K., Ramon, J., and Gärtner, T. (2006) · 2006
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., and Ponce, J. (2006) · 2006
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Graph kernels
Borgwardt, K. M. (2007) · 2007
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Graph kernels for disease outcome prediction from protein-protein interaction networks
Borgwardt, K. M., Kriegel, H.-P., Vishwanathan, S., and Schraudolph, N. N. (2007) · 2007
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The Pyramid Match Kernel: Efficient Learning with Sets of Features
Grauman, K., and Darrell, T. (2007) · 2007
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Learning Models of Relational MDPs Using Graph Kernels
Halbritter, F., and Geibel, P. (2007) · 2007
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Image Classification with Segmentation Graph Kernels
Harchaoui, Z., and Bach, F. (2007) · 2007
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Biological network comparison using graphlet degree distribution
Pržulj, N. (2007) · 2007
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Graph kernels between point clouds
Bach, F. R. (2008) · 2008
Cited alongside, same era.
Tree Kernels for Semantic Role Labeling
Moschitti, A., Pighin, D., and Basili, R. (2008) · 2008
Cited alongside, same era.
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
Riesen, K., and Bunke, H. (2008) · 2008
Cited alongside, same era.
Directed acyclic graph kernels for structural RNA analysis
Sato, K., Mituyama, T., Asai, K., and Sakakibara, Y. (2008) · 2008
Cited alongside, same era.
The optimal assignment kernel is not positive definite
Vert, J.-P. (2008) · 2008
Cited alongside, same era.
Comparison of descriptor spaces for chemical compound retrieval and classification
Wale, N., Watson, I., and Karypis, G. (2008) · 2008
Substructure counting graph kernels for machine learning from rdf data
De Vries, G. K. D., and de Rooij, S. (2015) · 2015
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P. (2015) · 2015
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Graph Invariant Kernels
Orsini, F., Frasconi, P., and De Raedt, L. (2015) · 2015
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Transitive Assignment Kernels for Structural Classification
Schiavinato, M., Gasparetto, A., and Torsello, A. (2015) · 2015
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Predicting Protein Function and Protein-Ligand Interaction with the 3D Neighborhood Kernel
Schietgat, L., Fannes, T., and Ramon, J. (2015) · 2015
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Halting in Random Walk Kernels
Sugiyama, M., and Borgwardt, K. M. (2015) · 2015
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Cited alongside, same era.
A Linear-time Graph Kernel
Hido, S., and Kashima, H. (2009) · 2009
Cited alongside, same era.
Graph kernels based on tree patterns for molecules
Mahé, P., and Vert, J.-P. (2009) · 2009
Cited alongside, same era.
Neural Network for Graphs: A Contextual Constructive Approachs
Micheli, A. (2009) · 2009
Cited alongside, same era.
The Graph Neural Network Model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G. (2009) · 2009
Cited alongside, same era.
Efficient Graphlet Kernels for Large Graph Comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K. M. (2009) · 2009
Cited alongside, same era.
Graph wavelet alignment kernels for drug virtual screening
Smalter, A., Huan, J., and Lushington, G. (2009) · 2009
Cited alongside, same era.
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Interaction Networks for Learning about Objects,Relations and Physics
Battaglia, P., Pascanu, R., Lai, M., Rezende, D. J., et al. (2016) · 2016
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016) · 2016
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The pyramid quantized Weisfeiler-Lehman graph representation
Gkirtzou, K., and Blaschko, M. B. (2016) · 2016
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Sub-network Based Kernels for Brain Network Classification
Jie, B., Liu, M., Jiang, X., and Zhang, D. (2016) · 2016
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Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels
Kanewala, U., Bieman, J. M., and Ben-Hur, A. (2016) · 2016
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Hadamard Code Graph Kernels for Classifying Graphs
Kataoka, T., and Inokuchi, A. (2016) · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P. (2016) · 2016
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Benchmark data sets for graph kernels.
Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., and Neumann, M. (2016) · 2016
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The Multiscale Laplacian Graph Kernel
Kondor, R., and Pan, H. (2016) · 2016
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On Valid Optimal Assignment Kernels and Applications to Graph Classification
Kriege, N. M., Giscard, P.-L., and Wilson, R. (2016) · 2016
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Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning
Massimo, C. M., Navarin, N., and Sperduti, A. (2016) · 2016
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Faster Kernels for Graphs with Continuous Attributes via Hashing
Morris, C., Kriege, N. M., Kersting, K., and Mutzel, P. (2016) · 2016
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Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection
Narayanan, A., Meng, G., Yang, L., Liu, J., and Chen, L. (2016) · 2016
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Propagation kernels: efficient graph kernels from propagated information
Neumann, M., Garnett, R., Bauckhage, C., and Kersting, K. (2016) · 2016
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Automatic architectural style detection using one-class support vector machines and graph kernels
Strobbe, T., Verstraeten, R., De Meyer, R., Van Campenhout, J., et al. (2016) · 2016
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Robust Visual Place Recognition with Graph Kernels
Stumm, E., Mei, C., Lacroix, S., Nieto, J., Hutter, M., and Siegwart, R. (2016) · 2016
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A Fast Kernel for Attributed Graphs
Su, Y., Han, F., Harang, R. E., and Yan, X. (2016) · 2016
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Learning molecular energies using localized graph kernels
Ferré, G., Haut, T., and Barros, K. (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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Chemoinformatics and stereoisomerism: A stereo graph kernel together with three new extensions
Grenier, P.-A., Brun, L., and Villemin, D. (2017) · 2017
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Inductive Representation Learning on Large Graphs
Hamilton, W., Ying, Z., and Leskovec, J. (2017) · 2017
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Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs
Morris, C., Kersting, K., and Mutzel, P. (2017) · 2017
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Matching Node Embeddings for Graph Similarity
Nikolentzos, G., Meladianos, P., and Vazirgiannis, M. (2017) · 2017
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Measuring the expressivity of graph kernels through statistical learning theory
Oneto, L., Navarin, N., Donini, M., Sperduti, A., Aiolli, F., and Anguita, D. (2017) · 2017
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KONG: Kernels for ordered-neighborhood graphs
Draief, M., Kutzkov, K., Scaman, K., and Vojnovic, M. (2018) · 2018
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The journey of graph kernels through two decades
Ghosh, S., Das, N., Goncalves, T., Quaresma, P., and Kundu, M. (2018) · 2018
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Graph Similarity and Approximate Isomorphism
Grohe, M., Rattan, G., and Woeginger, G. J. (2018) · 2018
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A Property Testing Framework for the Theoretical Expressivity of Graph Kernels
Kriege, N. M., Morris, C., Rey, A., and Sohler, C. (2018) · 2018
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Pre-training Graph Neural Networks with Kernels
Navarin, N., Tran, D. V., and Sperduti, A. (2018) · 2018
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A Degeneracy Framework for Graph Similarity
Nikolentzos, G., Meladianos, P., Limnios, S., and Vazirgiannis, M. (2018) · 2018
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Kernel Graph Convolutional Neural Networks
Nikolentzos, G., Meladianos, P., Tixier, A. J.-P., Skianis, K., and Vazirgiannis, M. (2018) · 2018
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Enhancing Graph Kernels via Successive Embeddings
Nikolentzos, G., and Vazirgiannis, M. (2018) · 2018
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Hierarchical Graph Representation Learning with Differentiable Pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J. (2018) · 2018
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DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Al-Rfou, R., Perozzi, B., and Zelle, D. (2019) · 2019
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Du, S. S., Hou, K., Salakhutdinov, R. R., Poczos, B., Wang, R., and Xu, K. (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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A Persistent Weisfeiler-Lehman Procedure for Graph Classification
Rieck, B., Bock, C., and Borgwardt, K. (2019) · 2019
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Wasserstein Weisfeiler-Lehman Graph Kernels
Togninalli, M., Ghisu, E., Llinares-López, F., Rieck, B., and Borgwardt, K. (2019) · 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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Graph Kernels: State-of-the-Art and Future Challenges
Borgwardt, K., Ghisu, E., Llinares-López, F., O’Bray, L., Rieck, B., et al. (2020) · 2020
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Convolutional Kernel Networks for Graph-Structured Data
Chen, D., Jacob, L., and Mairal, J. (2020) · 2020
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A Fair Comparison of Graph Neural Networks for Graph Classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A. (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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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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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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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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Deriving Neural Architectures from Sequence and Graph Kernels
Lei, T., Jin, W., Barzilay, R., and Jaakkola, T. (2017) · 2033
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Classification of small molecules by two-and three-dimensional decomposition kernels
Ceroni, A., Costa, F., and Frasconi, P. (2007) · 2045
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