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We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Weighted graph cuts without eigenvectors: A multilevel approach
I. S. Dhillon, Y. Guan, and B. Kulis · 2007
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Collective classification in network data
G. Sen, G. Namata, M. Bilgic, and L. Getoor · 2008
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A GPU algorithm for greedy graph matching
B. O. Fagginger Auer and R. H. Bisseling · 2011
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GDELT: Global data on events, location, and tone
K. Leetaru and P. A. Schrodt · 2013
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Machine learning of molecular electronic properties in chemical compound space
G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K. Müller, and O. A. von Lilienfeld · 2013
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FAUST: Dataset and evaluation for 3D mesh registration
F. Bogo, J. Romero, M. Loper, and M. J. Black · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld · 2014
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ICEWS coded event data
E. Boschee, J. Lautenschlager, S. O’Brien, S. Shellman, J. Starz, and M. Ward · 2015
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ShapeNet: An information-rich 3D model repository
A. X. Chang, T. Funkhouser, L. J. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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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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Benchmark data sets for graph kernels
K. Kersting, N. M. Kriege, C. Morris, P. Mutzel, and M. Neumann · 2016
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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Edge weight prediction in weighted signed networks
S. Kumar, F. Spezzano, V. Subrahmanian, and C. Faloutsos · 2016
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2016
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Geometric deep learning: Going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 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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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Geometric deep learning on graphs and manifolds using mixture model CNNs
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein · 2017
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Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Generating 3D faces using convolutional mesh autoencoders
A. Ranjan, T. Bolkart, S. Sanyal, and M. J. Black · 2018
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Modeling relational data with graph convolutional networks
M. S. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling · 2018
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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Attention-based graph neural network for semi-supervised learning
K. K. Thekumparampil, C. Wang, S. Oh, and L. Li · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Representation learning on graphs with jumping knowledge networks
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
M. Simonovsky and N. Komodakis · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. F. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, Ç. Gülçehre, F. Song, A. J. Ballard, J. Gilmer, G. E. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu · 2018
Cited alongside, same era.
Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking
A. Bojchevski and S. Günnemann · 2018
Cited alongside, same era.
A simple yet effective baseline for non-attribute graph classification
C. Cai and Y. Wang · 2018
Cited alongside, same era.
Towards sparse hierarchical graph classifiers
C. Cangea, P. Veličković, N. Jovanović, T. N. Kipf, and P. Liò · 2018
Cited alongside, same era.
Signed graph convolutional networks
T. Derr, Y. Ma, and J. Tang · 2018
Cited alongside, same era.
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
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Hierarchical graph representation learning with differentiable pooling
R. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec · 2018
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An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
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Graph neural networks with convolutional ARMA filters
F. M. Bianchi, D. Grattarola, L. Livi, and C. Alippi · 2019
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Just jump: Dynamic neighborhood aggregation in graph neural networks
M. Fey · 2019
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Recurrent event network for reasoning over temporal knowledge graphs
W. Jin, C. Zhang, P. Szekely, and X. Ren · 2019
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Predict then propagate: Graph neural networks meet personalized PageRank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2019
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Deep graph infomax
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjeml · 2019
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Simplifying graph convolutional networks
F. Wu, T. Zhang, A. H. de Souza Jr., C. Fifty, T. Yu, and K. Q. Weinberger · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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