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Graph pooling is a central component of a myriad of graph neural network (GNN) architectures.
Weighted graph cuts without eigenvectors: a multilevel approach
I. S. Dhillon, Y. Guan, and B. Kulis · 2007
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Comparison of descriptor spaces for chemical compound retrieval and classification
N. Wale, I. A. Watson, and G. Karypis · 2008
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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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SLIC superpixels compared to state-of-the-art superpixel methods
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk · 2012
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun · 2014
Earlier work this paper cites.
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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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Order matters: Sequence to sequence for sets
M. Kudlur O. Vinyals, S. Bengio · 2015
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Striving for Simplicity: The All Convolutional Net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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Deep graph kernels
P. Yanardag and S. V. N. Vishwanathan · 2015
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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. Hamilton, Z. 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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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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Random shifting for CNN: a solution to reduce information loss in down-sampling layers
G. Zhao, J. Wang, and Z. Zhang · 2017
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A simple yet effective baseline for non-attribute graph classification
C. Cai and Y. Wang · 2018
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 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.
Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
Cited alongside, same era.
Large-scale point cloud semantic segmentation with superpoint graphs
L. Landrieu and M. Simonovsky · 2018
Cited alongside, same era.
Graph convolutional networks with eigenpooling
Y. Ma, S. Wang, C. C. Aggarwal, and J. Tang · 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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Relational pooling for graph representations
R. Murphy, B. Srinivasan, V. Rao, and B. Ribeiro · 2019
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Revisiting graph neural networks: All we have is low-pass filters
H. NT and T. Maehara · 2019
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Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
C. Y. Wee, C. Liu, A. Lee, J. S. Poh, H. Ji, and A. Qiu · 2019
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Attention is not not explanation
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S. Rhee, S. Seo, and S. Kim · 2018
Cited alongside, same era.
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
A. Ruderman, N. C. Rabinowitz, A. S. Morcos, and D. Zoran · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
Cited alongside, same era.
EasyConvPooling: Random pooling with easy convolution for accelerating training and testing
J. Sheng, C. Chen, C. Fu, and C. J. Xue · 2018
Cited alongside, same era.
Are powerful graph neural nets necessary? A dissection on graph classification
T. Chen, S. Bian, and Y. Sun · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
Cited alongside, same era.
Graph U-nets
H. Gao and S. Ji · 2019
Cited alongside, same era.
S. Wiegreffe and Y. Pinter · 2019
Later among the works it cites.
Simplifying graph convolutional networks
F. Wu, A. H. Souza Jr, T. Zhang, C. Fifty, T. Yu, and K. 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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Spectral clustering with graph neural networks for graph pooling
F. M. Bianchi, D. Grattarola, and C. Alippi · 2020
Closest in time.
Benchmarking graph neural networks
V. P. Dwivedi, C. K. Joshi, T. Laurent, Y. Bengio, and X. Bresson · 2020
Closest in time.
A fair comparison of graph neural networks for graph classification
F. Errica, M. Podda, D. Bacciu, and A. Micheli · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
Closest in time.
Memory-based graph networks
A. H. Khasahmadi, K. Hassani, P. Moradi, L. Lee, and Q. Morris · 2020
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TU dataset: 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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StructPool: Structured graph pooling via random fields
H. Yuan and S. Ji · 2020
Closest in time.