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The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.
“Kernels and regularization on graphs,”
A. Smola and R. Kondor, · 2003
Earlier work this paper cites.
“Semi-supervised learning with graphs,”
X. Zhu, · 2005
Earlier work this paper cites.
Semi-supervised learning
O. Chapelle, B. Schölkopf, and A. Zien, · 2006
Earlier work this paper cites.
“Modularity and community structure in networks,”
M. E. J. Newman, · 2006
Earlier work this paper cites.
“A tutorial on spectral clustering,”
U. von Luxburg, · 2007
Earlier work this paper cites.
“Proto-value functions: A laplacian framework for learning representation and control in markov decision processes,”
S. Mahadevan and M. Maggioni, · 2007
Earlier work this paper cites.
“Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data,”
O. Banerjee, L. El Ghaoui, and A. d’Aspremont, · 2008
Earlier work this paper cites.
“Sparse inverse covariance estimation with the graphical Lasso,”
J. Friedman, T. Hastie, and R. Tibshirani, · 2008
Earlier work this paper cites.
“Community detection in graphs,”
S. Fortunato, · 2010
Earlier work this paper cites.
“Multi-scale modularity in complex networks,”
R. Lambiotte, · 2010
Earlier work this paper cites.
“Inferring networks of diffusion and influence,”
M. Gomez-Rodriguez, J. Leskovec, and A. Krause, · 2010
Earlier work this paper cites.
“On the convexity of latent social network inference,”
S. A. Myers and J. Leskovec, · 2010
Earlier work this paper cites.
“Wavelets on graphs via spectral graph theory,”
D. K. Hammond, P. Vandergheynst, and R. Gribonval, · 2011
Earlier work this paper cites.
“Opinion dynamics and learning in social networks,”
D. Acemoglu and A. Ozdaglar, · 2011
Earlier work this paper cites.
“Kernels for vector-valued functions: A review,”
M. A. Álvarez, L. Rosasco, and N. D. Lawrence, · 2012
Earlier work this paper cites.
“ImageNet classification with deep convolutional neural networks,”
A. Krizhevsky, I. Sutskever, and G. Hinton, · 2012
Earlier work this paper cites.
“The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,”
D. I Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, · 2013
Earlier work this paper cites.
“Discrete signal processing on graphs,”
A. Sandryhaila and J. M. F. Moura, · 2013
Earlier work this paper cites.
“Cooperative optimal control for multi-agent systems on directed graph topologies,”
K. H. Movric and F. L. Lewis, · 2013
Earlier work this paper cites.
“Spectral networks and deep locally connected networks on graphs,”
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, · 2014
Earlier work this paper cites.
“Graph wavelets for multiscale community mining,”
N. Tremblay and P. Borgnat, · 2014
Earlier work this paper cites.
“Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure,”
A. Sandryhaila and J. M.F. Moura, · 2014
Earlier work this paper cites.
“Distributed cooperative optimal control for multiagent systems on directed graphs: An inverse optimal approach,”
H. Zhang, T. Feng, G.-H. Yang, and H. Liang, · 2014
Earlier work this paper cites.
“A review of relational machine learning for knowledge graphs,”
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, · 2016
Earlier work this paper cites.
“Convolutional neural networks on graphs with fast localized spectral filtering,”
M. Defferrard, X. Bresson, and P. Vandergheynst, · 2016
Earlier work this paper cites.
“Compressive spectral clustering,”
N. Tremblay, G. Puy, R. Gribonval, and P. Vandergheynst, · 2016
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“Variational graph auto-encoders,”
T. N. Kipf and M. Welling, · 2016
Cited alongside, same era.
“Learning Laplacian matrix in smooth graph signal representations,”
X. Dong, D. Thanou, P. Frossard, and P. Vandergheynst, · 2016
Cited alongside, same era.
“How to learn a graph from smooth signals,”
V. Kalofolias, · 2016
Cited alongside, same era.
“Higher-order organization of complex networks,”
A. R Benson, D. F Gleich, and J. Leskovec, · 2016
Cited alongside, same era.
“Geometric deep learning: Going beyond euclidean data,”
M. M Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, · 2017
Cited alongside, same era.
“Spectral MAB for unknown graph processes,”
L. Toni and P. Frossard, · 2018
Later among the works it cites.
“Compressive spectral clustering,”
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, · 2018
Later among the works it cites.
“Relational inductive biases, deep learning, and graph networks,”
P. W. Battaglia et al., · 2018
Later among the works it cites.
“Motifnet: A motif-based graph convolutional network for directed graphs,”
F. Monti, K. Otness, and M. M Bronstein, · 2018
Later among the works it cites.
“String v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,”
D. Szklarczyk et al., · 2019
Later among the works it cites.
“Connecting the dots: Identifying network structure via graph signal processing,”
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“Semi-supervised classification with graph convolutional networks,”
T. N. Kipf and M. Welling, · 2017
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“Geometric matrix completion with recurrent multi-graph neural networks,”
F. Monti, M. M. Bronstein, and X. Bresson, · 2017
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“Filtering random graph processes over random time-varying graphs,”
E. Isufi, A. Loukas, A. Simonetto, and G. Leus, · 2017
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“Graph learning from data under structural and Laplacian constraints,”
H. E. Egilmez, E. Pavez, and A. Ortega, · 2017
Cited alongside, same era.
“Learning sparse graphs under smoothness prior,”
S. P. Chepuri, S. Liu, G. Leus, and A. O. Hero, · 2017
Cited alongside, same era.
“Learning heat diffusion graphs,”
D. Thanou, X. Dong, D. Kressner, and P. Frossard, · 2017
Cited alongside, same era.
G. Mateos, S. Segarra, A. G. Marques, and A. Ribeiro, · 2019
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“Learning graphs from data: A signal representation perspective,”
X. Dong, D. Thanou, M. Rabbat, and P. Frossard, · 2019
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“Robust deep graph based learning for binary classification,”
M. Ye, V. Stankovic, L. Stankovic, and G. Cheung, · 2019
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“Introducing graph smoothness loss for training deep learning architectures,”
M. Bontonou, C. Lassance, G. B. Hacene, V. Gripon, J. Tang, and A. Ortega, · 2019
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“Laplacian power networks: Bounding indicator function smoothness for adversarial defense,”
C. E. R. K. Lassance, V. Gripon, and A. Ortega, · 2019
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“Relational generalized few-shot learning,”
X. Shi, L. Salewski, M. Schiegg, Z. Akata, and M. Welling, · 2019
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“Transferability of spectral graph convolutional neural networks,”
R. Levie, M. M Bronstein, and G. Kutyniok, · 2019
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“Stability properties of graph neural networks,”
F. Gama, J. Bruna, and A. Ribeiro, · 2019
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“Dynamic graph CNN for learning on point clouds,”
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, · 2019
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“Modelling graph errors: Towards robust graph signal processing,”
J. Miettinen, S. A. Vorobyov, and E. Ollila, · 2019
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“Bayesian graph convolutional neural networks for semi-supervised classification,”
Y. Zhang, S. Pal, M. Coates, and D. Üstebay, · 2019
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“Topological signal processing over simplicial complexes,”
S. Barbarossa and S. Sardellitti, · 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, · 2020
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“Approximating spectral clustering via sampling: A review,”
N. Tremblay and A. Loukas, · 2020
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“Gaussian processes on graphs via spectral kernel learning,”
Y.-C. Zhi, N. C. Cheng, and X. Dong, · 2020
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“Understanding graph neural networks from graph signal denoising perspectives,”
G. Fu, Y. Hou, J. Zhang, K. Ma, B. F. Kamhoua, and J. Cheng, · 2020
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“Zero shot learning with the isoperimetric loss,”
S. Deutsch, A. L. Bertozzi, and S. Soatto, · 2020
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“Multitask learning over graphs: An approach for distributed, streaming machine mearning,”
R. Nassif, S. Vlaski, C. Richard, J. Chen, and A. H. Sayed, · 2020
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“On the stability of polynomial spectral graph filters,”
H. Kenlay, D. Thanou, and X. Dong, · 2020
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“Deep unsupervised learning of 3D point clouds via graph topology inference and filtering,”
S. Chen, C. Duan, Y. Yang, C. Feng, D. Li, and D. Tian, · 2020
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