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Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
On the universality of invariant networks
H. Maron, E. Fetaya, N. Segol, and Y. Lipman · 1901
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Deepgcns: Making gcns go as deep as cnns
G. Li, M. Müller, G. Qian, I. C. Delgadillo, A. Abualshour, A. K. Thabet, and B. Ghanem · 1910
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Sur la notion de la moyenne
A. N. Kolmogorov and G. Castelnuovo · 1930
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A reduction of a graph to a canonical form and an algebra arising during this reduction
B. Weisfeiler and A. A. Lehman · 1968
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A tutorial on energy-based learning
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang · 2006
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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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Relational learning via latent social dimensions
L. Tang and H. Liu · 2009
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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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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Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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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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J. L. Ba, J. R. Kiros, and G. E. Hinton · 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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node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Z. Yang, W. W. Cohen, and R. Salakhutdinov · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Cited alongside, same era.
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
I. Armeni, A. Sax, A. R. Zamir, and S. Savarese · 2017
Cited alongside, same era.
On the properties of the softmax function with application in game theory and reinforcement learning
Adaptive graph convolutional neural networks
R. Li, S. Wang, F. Zhu, and J. Huang · 2018
Later among the works it cites.
Semi-supervised user geolocation via graph convolutional networks
A. Rahimi, T. Cohn, and T. Baldwin · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Later among the works it cites.
Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
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B. Gao and L. Pavel · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
M. Zitnik and J. Leskovec · 2017
Cited alongside, same era.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Later among the works it cites.
Gaan: Gated attention networks for learning on large and spatiotemporal graphs
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D. Yeung · 2018
Later among the works it cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
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Universal invariant and equivariant graph neural networks
N. Keriven and G. Peyré · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2019
Later among the works it cites.
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
Later among the works it cites.
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.
Dropedge: Towards deep graph convolutional networks on node classification
Y. Rong, W. Huang, T. Xu, and J. Huang · 2020
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GraphSAINT: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2020
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Pairnorm: Tackling oversmoothing in gnns
L. Zhao and L. Akoglu · 2020
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