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Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs.
Edge contraction pooling for graph neural networks
F. Diehl · 1905
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Understanding attention in graph neural networks
B. Knyazev, G. W. Taylor, and M. R. Amer · 1905
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Metis: Unstructured graph partitioning and sparse matrix ordering system
G. Karypis · 1997
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Fast multiscale clustering and manifold identification
D. Kushnir, M. Galun, and A. Brandt · 2006
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Weighted graph cuts without eigenvectors a multilevel approach
I. S. Dhillon, Y. Guan, and B. Kulis · 2007
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A tutorial on spectral clustering
U. Von Luxburg · 2007
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Selecting receptive fields in deep networks
A. Coates and A. Y. Ng · 2011
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Kron reduction of graphs with applications to electrical networks
F. Dorfler and F. Bullo · 2012
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2015
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 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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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
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Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 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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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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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
R. Levie, F. Monti, X. Bresson, and M. M. Bronstein · 2017
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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
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. Osman Ulusoy, and A. Geiger · 2017
Cited alongside, same era.
Dynamic edgeconditioned filters in convolutional neural networks on graphs
M. Simonovsky and N. Komodakis · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
Cited alongside, same era.
Graph reduction with spectral and cut guarantees
A. Loukas · 2019
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Clique pooling for graph classification
E. Luzhnica, B. Day, and P. Lio · 2019
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Graph convolutional networks with eigenpooling
Y. Ma, S. Wang, C. C. Aggarwal, and J. Tang · 2019
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On the universality of invariant networks
H. Maron, E. Fetaya, N. Segol, and Y. Lipman · 2019
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Universal readout for graph convolutional neural networks
N. Navarin, D. Van Tran, and A. Sperduti · 2019
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Towards interpretable sparse graph representation learning with laplacian pooling
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C. Cangea, P. Velićković, N. Jovanović, T. Kipf, and P. Liò · 2018
Cited alongside, same era.
Splinecnn: Fast geometric deep learning with continuous b-spline kernels
M. Fey, J. E. Lenssen, F. Weichert, and H. Müller · 2018
Cited alongside, same era.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
Cited alongside, same era.
A non-negative factorization approach to node pooling in graph convolutional neural networks
D. Bacciu and L. Di Sotto · 2019
Cited alongside, same era.
Unsupervised inductive graph-level representation learning via graph-graph proximity
Y. Bai, H. Ding, Y. Qiao, A. Marinovic, K. Gu, T. Chen, Y. Sun, and W. Wang · 2019
Cited alongside, same era.
E. Noutahi, D. Beani, J. Horwood, and P. Tossou · 2019
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Asap: Adaptive structure aware pooling for learning hierarchical graph representations
E. Ranjan, S. Sanyal, and P. P. Talukdar · 2019
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Net: Degree-specific graph neural networks for node and graph classification
J. Wu, J. He, and J. Xu · 2019
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Deep graph mapper: Seeing graphs through the neural lens
C. Bodnar, C. Cangea, and P. Liò · 2020
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Rethinking pooling in graph neural networks
D. Mesquita, A. Souza, and S. Kaski · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2020
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A survey on the expressive power of graph neural networks
R. Sato · 2020
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Graph convolutional networks with multi-level coarsening for graph classification
Y. Xie, C. Yao, M. Gong, C. Chen, and A. Qin · 2020
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Design space for graph neural networks
J. You, Z. Ying, and J. Leskovec · 2020
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Structpool: Structured graph pooling via conditional random fields
H. Yuan and S. Ji · 2020
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Graph coarsening with neural networks
C. Cai, D. Wang, and Y. Wang · 2021
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Graph neural networks in tensorflow and keras with spektral
D. Grattarola and C. Alippi · 2021
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Hierarchical multi-view graph pooling with structure learning
Z. Zhang, J. Bu, M. Ester, J. Zhang, Z. Li, C. Yao, D. Huifen, Z. Yu, and C. Wang · 2021
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