Fetching the paper…
Reading the bibliography…
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications.
The status of multiple comparisons: simultaneous estimation of all pairwise comparisons in one-way anova designs
M. R. Stoline · 1981
Earlier work this paper cites.
Collective dynamics of ‘small-world’networks
D. J. Watts and S. H. Strogatz · 1998
Earlier work this paper cites.
The pagerank citation ranking: Bringing order to the web
L. Page, S. Brin, R. Motwani, and T. Winograd · 1999
Earlier work this paper cites.
Growing scale-free networks with tunable clustering
P. Holme and B. J. Kim · 2002
Earlier work this paper cites.
Bonferroni correction
E. W. Weisstein · 2004
Earlier work this paper cites.
The kendall rank correlation coefficient
H. Abdi · 2007
Earlier work this paper cites.
Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Benchmark data sets for graph kernels, 2016
K. Kersting, N. M. Kriege, C. Morris, P. Mutzel, and M. Neumann · 2016
Earlier work this paper cites.
Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Earlier work this paper cites.
Searching for activation functions
P. Ramachandran, B. Zoph, and Q. V. Le · 2017
Cited alongside, same era.
The marginal value of adaptive gradient methods in machine learning
A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
M. Zitnik and J. Leskovec · 2017
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
Cited alongside, same era.
How does batch normalization help optimization?
S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
Relational pooling for graph representations
R. L. Murphy, B. Srinivasan, V. Rao, and B. Ribeiro · 2019
Later among the works it cites.
Meta architecture search
A. Shaw, W. Wei, W. Liu, L. Song, and B. Dai · 2019
Later among the works it cites.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Later among the works it cites.
GNNExplainer: Generating explanations for graph neural networks
Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
Later among the works it cites.
Hierarchical temporal convolutional networks for dynamic recommender systems
J. You, Y. Wang, A. Pal, P. Eksombatchai, C. Rosenburg, and J. Leskovec · 2019
Later among the works it cites.
G2SAT: Learning to generate sat formulas
J. You, H. Wu, C. Barrett, R. Ramanujan, and J. Leskovec · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
Cited alongside, same era.
Transfer learning with neural automl
C. Wong, N. Houlsby, Y. Lu, and A. Gesmundo · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec · 2018
Cited alongside, same era.
Position-aware graph neural networks
J. You, R. Ying, and J. Leskovec · 2019
Later among the works it cites.
Circuit-gnn: Graph neural networks for distributed circuit design
G. Zhang, H. He, and D. Katabi · 2019
Later among the works it cites.
Auto-gnn: Neural architecture search of graph neural networks
K. Zhou, Q. Song, X. Huang, and X. Hu · 2019
Later among the works it cites.
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
Closest in time.
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.
Designing network design spaces
I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, and P. Dollár · 2020
Closest in time.
Neural execution of graph algorithms
P. Velikovi, R. Ying, M. Padovano, R. Hadsell, and C. Blundell · 2020
Closest in time.
Neural subgraph matching
R. Ying, Z. Lou, J. You, C. Wen, A. Canedo, and J. Leskovec · 2020
Closest in time.
Graph structure of neural networks
J. You, J. Leskovec, K. He, and S. Xie · 2020
Closest in time.
Handling missing data with graph representation learning
J. You, X. Ma, D. Ding, M. Kochenderfer, and J. Leskovec · 2020
Closest in time.