Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have been studied through the lens of expressive power and generalization.
A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
Earlier work this paper cites.
Automatic generation of complementary descriptors with molecular graph networks
Merkwirth, C. and Lengauer, T · 2005
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Saxe, A. M., McClelland, J. L., and Ganguli, S · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Earlier work this paper cites.
Deep learning without poor local minima
Kawaguchi, K · 2016
Earlier work this paper cites.
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
Earlier work this paper cites.
Identity matters in deep learning
Hardt, M. and Ma, T · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
On the optimization of deep networks: Implicit acceleration by overparameterization
Arora, S., Cohen, N., and Hazan, E · 2018
Earlier work this paper cites.
Adaptive sampling towards fast graph representation learning
Huang, W., Zhang, T., Rong, Y., and Huang, J · 2018
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
Earlier work this paper cites.
Deep linear networks with arbitrary loss: All local minima are global
Laurent, T. and Brecht, J · 2018
Earlier work this paper cites.
Learning overparameterized neural networks via stochastic gradient descent on structured data
Li, Y. and Liang, Y · 2018
Earlier work this paper cites.
The vapnik–chervonenkis dimension of graph and recursive neural networks
Scarselli, F., Tsoi, A. C., and Hagenbuchner, M · 2018
Earlier work this paper cites.
Attention-based graph neural network for semi-supervised learning
Thekumparampil, K. K., Wang, C., Oh, S., and Li, L.-J · 2018
Earlier work this paper cites.
Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z · 2019
Cited alongside, same era.
Gradient descent with identity initialization efficiently learns positive-definite linear transformations by deep residual networks
Bartlett, P. L., Helmbold, D. P., and Long, P. M · 2019
Cited alongside, same era.
On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
N-gcn: Multi-scale graph convolution for semi-supervised node classification
Abu-El-Haija, S., Kapoor, A., Perozzi, B., and Lee, J · 2020
Later among the works it cites.
Graphnorm: A principled approach to accelerating graph neural network training
Cai, T., Luo, S., Xu, K., He, D., Liu, T.-y., and Wang, L · 2020
Later among the works it cites.
Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
Later among the works it cites.
Generalization and representational limits of graph neural networks
Garg, V. K., Jegelka, S., and Jaakkola, T · 2020
Later among the works it cites.
Dynamics of deep neural networks and neural tangent hierarchy
Huang, J. and Yau, H.-T · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
Cited alongside, same era.
On lazy training in differentiable programming
Chizat, L., Oyallon, E., and Bach, F · 2019
Cited alongside, same era.
Width provably matters in optimization for deep linear neural networks
Du, S. and Hu, W · 2019
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
Cited alongside, same era.
Gradient descent finds global minima for generalizable deep neural networks of practical sizes
Kawaguchi, K. and Huang, J · 2019
Cited alongside, same era.
Universal invariant and equivariant graph neural networks
Keriven, N. and Peyré, G · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Lee, J., Xiao, L., Schoenholz, S., Bahri, Y., Novak, R., Sohl-Dickstein, J., and Pennington, J · 2019
Cited alongside, same era.
Ji, Z. and Telgarsky, M · 2020
Later among the works it cites.
Deepergcn: All you need to train deeper gcns
Li, G., Xiong, C., Thabet, A., and Ghanem, B · 2020
Later among the works it cites.
Graph adversarial networks: Protecting information against adversarial attacks
Liao, P., Zhao, H., Xu, K., Jaakkola, T., Gordon, G., Jegelka, S., and Salakhutdinov, R · 2020
Later among the works it cites.
On the linearity of large non-linear models: when and why the tangent kernel is constant
Liu, C., Zhu, L., and Belkin, M · 2020
Later among the works it cites.
How hard is to distinguish graphs with graph neural networks?
Loukas, A · 2020
Later among the works it cites.
Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks
Oono, K. and Suzuki, T · 2020
Later among the works it cites.
Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2020
Later among the works it cites.
Building powerful and equivariant graph neural networks with message-passing
Vignac, C., Loukas, A., and Frossard, P · 2020
Later among the works it cites.
What can neural networks reason about?
Xu, K., Li, J., Zhang, M., Du, S. S., ichi Kawarabayashi, K., and Jegelka, S · 2020
Later among the works it cites.
Fast learning of graph neural networks with guaranteed generalizability: One-hidden-layer case
Zhang, S., Wang, M., Liu, S., Chen, P.-Y., and Xiong, J · 2020
Later among the works it cites.
On the global convergence of training deep linear resnets
Zou, D., Long, P. M., and Gu, Q · 2020
Later among the works it cites.
On the theory of implicit deep learning: Global convergence with implicit layers
Kawaguchi, K · 2021
Closest in time.
Optimal rates for averaged stochastic gradient descent under neural tangent kernel regime
Nitanda, A. and Suzuki, T · 2021
Closest in time.
How neural networks extrapolate: From feedforward to graph neural networks
Xu, K., Zhang, M., Li, J., Du, S. S., Kawarabayashi, K.-I., and Jegelka, S · 2021
Closest in time.