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Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
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Diffusion maps
R. R. Coifman and S. Lafon · 2006
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Diffusion wavelets
R. R. Coifman and M. Maggioni · 2006
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A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y.-W. Teh · 2006
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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The block grade of a block krylov space
M. H. Gutknecht and T. Schmelzer · 2009
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Wavelets on graphs via spectral graph theory
D. K. Hammond, P. Vandergheynst, and R. Gribonval · 2011
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D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2012
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Learning with partially absorbing random walks
X.-M. Wu, Z. Li, A. M. So, J. Wright, and S.-F. Chang · 2012
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Sparse random graphs: Eigenvalues and eigenvectors
L. V. Tran, V. H. Vu, and K. Wang · 2013
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Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2015
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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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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2015
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Improved analyses of the randomized power method and block lanczos method
S. Wang, Z. Zhang, and T. Zhang · 2015
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Geometric deep learning on graphs and manifolds using mixture model cnns
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein · 2017
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
J. Chen, T. Ma, and C. Xiao · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X. Wu · 2018
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Adaptive graph convolutional neural networks
R. Li, S. Wang, F. Zhu, and J. Huang · 2018
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T. N. Kipf and M. Welling · 2016
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Revisiting semi-supervised learning with graph embeddings
Z. Yang, W. W. Cohen, and R. Salakhutdinov · 2016
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2017
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The radau–lanczos method for matrix functions
A. Frommer, K. Lund, M. Schweitzer, and D. B. Szyld · 2017
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Block Krylov subspace methods for functions of matrices
A. Frommer, K. Lund, and D. B. Szyld · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Graph partition neural networks for semi-supervised classification
R. Liao, M. Brockschmidt, D. Tarlow, A. L. Gaunt, R. Urtasun, and R. Zemel · 2018
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Stability of the lanczos method for matrix function approximation
C. Musco, C. Musco, and A. Sidford · 2018
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Graph convolutional networks: Algorithms, applications and open challenges
S. Zhang, H. Tong, J. Xu, and R. Maciejewski · 2018
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Lanczosnet: Multi-scale deep graph convolutional networks
R. Liao, Z. Zhao, R. Urtasun, and R. S. Zemel · 2019
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Virtual adversarial training on graph convolutional networks in node classification
K. Sun, H. Guo, Z. Zhu, and Z. Lin · 2019
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Multi-stage self-supervised learning for graph convolutional networks
K. Sun, Z. Zhu, and Z. Lin · 2019
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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