Fetching the paper…
Reading the bibliography…
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs.
Functions of positive and negative type, and their connection with the theory of integral equations
Mercer, J. 1909 · 1909
Earlier work this paper cites.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Weisfeiler, B.; and Lehman, A. 1968 · 1968
Earlier work this paper cites.
Conjugate Duality and Optimization
Rockafellar, R. T. 1974 · 1974
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K.; Stinchcombe, M.; and White, H. 1989 · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K. 1991 · 1991
Earlier work this paper cites.
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. 1998 · 1998
Earlier work this paper cites.
ARPACK USERS GUIDE: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods
Lehoucq, R. B.; Sorensen, D. C.; and Yang, C. 1998 · 1998
Earlier work this paper cites.
Statistical Learning Theory
Vapnik, V. 1998 · 1998
Earlier work this paper cites.
Least Squares Support Vector Machine Classifiers
Suykens, J. A. K.; and Vandewalle, J. 1999 · 1999
Earlier work this paper cites.
Learning with Kernels
Schölkopf, B.; and Smola, A. 2002 · 2002
Earlier work this paper cites.
Least Squares Support Vector Machines
Suykens, J. A. K.; Van Gestel, T.; De Brabanter, J.; De Moor, B.; and Vandewalle, J. 2002 · 2002
Earlier work this paper cites.
A Note on the Universal Approximation Capability of Support Vector Machines
Hammer, B.; and Gersmann, K. 2003 · 2003
Earlier work this paper cites.
Kernels and Regularization on Graphs
Smola, A. J.; and Kondor, R. 2003 · 2003
Earlier work this paper cites.
A support vector machine formulation to PCA analysis and its Kernel version
Suykens, J. A. K.; Van Gestel, T.; Vandewalle, J.; and De Moor, B. 2003 · 2003
Earlier work this paper cites.
Convex Optimization
Boyd, S.; and Vandenberghe, L. 2004 · 2004
Earlier work this paper cites.
Numerical solution of saddle point problems
Benzi, M.; Golub, G. H.; and Liesen, J. 2005 · 2005
Earlier work this paper cites.
Kernel K-Means for Categorical Data
Couto, J. 2005 · 2005
Earlier work this paper cites.
Support Vector Machines
Christmann, A.; and Steinwart, I. 2008 · 2008
Cited alongside, same era.
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.
Learning Deep Architectures for AI
Bengio, Y. 2009 · 2009
Cited alongside, same era.
Soft Kernel Spectral Clustering
Langone, R.; Mall, R.; and Suykens, J. A. K. 2013 · 2013
Cited alongside, same era.
Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering
Mehrkanoon, S.; Alzate, C.; Mall, R.; Langone, R.; and Suykens, J. A. K. 2015 · 2015
Cited alongside, same era.
Learning Deep Generative Models
Salakhutdinov, R. 2015 · 2015
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
Later among the works it cites.
A Gentle Introduction to Deep Learning for Graphs
Bacciu, D.; Errica, F.; Micheli, A.; and Podda, M. 2020 · 2020
Later among the works it cites.
Convolutional Kernel Networks for Graph-Structured Data
Chen, D.; Jacob, L.; and Mairal, J. 2020 · 2020
Later among the works it cites.
Graph Representation Learning
Hamilton, W. L. 2020 · 2020
Later among the works it cites.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2020
Later among the works it cites.
A survey on graph kernels
Kriege, N. M.; Johansson, F. D.; and Morris, C. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yang, Z.; Cohen, W.; and Salakhudinov, R. 2016 · 2016
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Deriving Neural Architectures from Sequence and Graph Kernels
Lei, T.; Jin, W.; Barzilay, R.; and Jaakkola, T. 2017 · 2017
Cited alongside, same era.
Deep Restricted Kernel Machines Using Conjugate Feature Duality
Suykens, J. A. K. 2017 · 2017
Cited alongside, same era.
To understand deep learning we need to understand kernel learning
Belkin, M.; Ma, S.; and Mandal, S. 2018 · 2018
Cited alongside, same era.
The journey of graph kernels through two decades
Ghosh, S.; Das, N.; Gonçalves, T.; Quaresma, P.; and Kundu, M. 2018 · 2018
Cited alongside, same era.
Adaptive Universal Generalized PageRank Graph Neural Network
Chien, E.; Peng, J.; Li, P.; and Milenkovic, O. 2021 · 2021
Later among the works it cites.
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
He, M.; Wei, Z.; Huang, Z.; and Xu, H. 2021 · 2021
Later among the works it cites.
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Lim, D.; Hohne, F.; Li, X.; Huang, S. L.; Gupta, V.; Bhalerao, O.; and Lim, S.-N. 2021 · 2021
Later among the works it cites.
Multi-Scale attributed node embedding
Rozemberczki, B.; Allen, C.; and Sarkar, R. 2021 · 2021
Later among the works it cites.
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
Tonin, F.; Patrinos, P.; and Suykens, J. A. K. 2021 · 2021
Later among the works it cites.
A Comprehensive Survey on Graph Neural Networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2021 · 2021
Later among the works it cites.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Feng, A.; You, C.; Wang, S.; and Tassiulas, L. 2022 · 2022
Later among the works it cites.
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited
He, M.; Wei, Z.; and Wen, J.-R. 2022 · 2022
Later among the works it cites.
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
Tao, Q.; Tonin, F.; Patrinos, P.; and Suykens, J. A. K. 2022 · 2022
Later among the works it cites.
Graph Clustering with Graph Neural Networks
Tsitsulin, A.; Palowitch, J.; Perozzi, B.; and Müller, E. 2023 · 2023
Closest in time.
Disentangled Representation Learning and Generation With Manifold Optimization
Pandey, A.; Fanuel, M.; Schreurs, J.; and Suykens, J. A. K. 2022 · 2036
Closest in time.