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Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Weighted graph cuts without eigenvectors a multilevel approach
Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Contour detection and hierarchical image segmentation
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep convolutional networks on graph-structured data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Attribute-graph: A graph based approach to image ranking
Nikita Prabhu and R Venkatesh Babu · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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On valid optimal assignment kernels and applications to graph classification
Nils M Kriege, Pierre-Louis Giscard, and Richard Wilson · 2016
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Semantic object parsing with graph lstm
Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, and Shuicheng Yan · 2016
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Dynamic edgeconditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Iterative visual reasoning beyond convolutions
Xinlei Chen, Li-Jia Li, Li Fei-Fei, and Abhinav Gupta · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2018
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Visual relationship detection with language priors
Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Graph-based isometry invariant representation learning
Renata Khasanova and Pascal Frossard · 2017
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Spectral multigraph networks for discovering and fusing relationships in molecules
Boris Knyazev, Xiao Lin, Mohamed R Amer, and Graham W Taylor · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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Graph U-Net
Hongyang Gao and Shuiwang Ji · 2019
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Deep graph infomax
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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