Fetching the paper…
Reading the bibliography…
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
Earlier work this paper cites.
A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 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.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 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., Ying, Z., and Leskovec, J · 2017
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. and Welling, M · 2017
Earlier work this paper cites.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
Earlier work this paper cites.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
Simonovsky, M. and Komodakis, N · 2017
Earlier work this paper cites.
Residual gated graph convnets
Bresson, X. and Laurent, T · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X · 2018
Earlier work this paper cites.
Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
Earlier work this paper cites.
Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., and Leskovec, J · 2018
Earlier work this paper cites.
Universal invariant and equivariant graph neural networks
Keriven, N. and Peyré, G · 2019
Earlier work this paper cites.
Diffusion improves graph learning
Klicpera, J., Weißenberger, S., and Günnemann, S · 2019
Earlier work this paper cites.
Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
Earlier work this paper cites.
Weisfeiler and Leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
Cited alongside, same era.
Dynamic graph CNN for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
Cited alongside, same era.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2019
Cited alongside, same era.
Probabilistic learning on graphs via contextual architectures
Bacciu, D., Errica, F., and Micheli, A · 2020
Cited alongside, same era.
Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T · 2021
Later among the works it cites.
Shortest path networks for graph property prediction
Abboud, R., Dimitrov, R., and Ceylan, İ. İ · 2022
Later among the works it cites.
Equivariant subgraph aggregation networks
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M. M., and Maron, H · 2022
Later among the works it cites.
The infinite contextual graph Markov model
Castellana, D., Errica, F., Bacciu, D., and Micheli, A · 2022
Later among the works it cites.
Towards robust graph neural networks for noisy graphs with sparse labels
Dai, E., Jin, W., Liu, H., and Wang, S · 2022
Later among the works it cites.
Long range graph benchmark
Dwivedi, V. P., Rampášek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
Cited alongside, same era.
A fair comparison of graph neural networks for graph classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A · 2020
Cited alongside, same era.
Graph representation learning
Hamilton, W. L · 2020
Cited alongside, same era.
Policy-GNN: Aggregation optimization for graph neural networks
Lai, K.-H., Zha, D., Zhou, K., and Hu, X · 2020
Cited alongside, same era.
What graph neural networks cannot learn: depth vs width
Loukas, A · 2020
Cited alongside, same era.
Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
Cited alongside, same era.
Asynchronous neural networks for learning in graphs
Faber, L. and Wattenhofer, R · 2022
Later among the works it cites.
Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Giovanni, F. D., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2022
Later among the works it cites.
Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in GNNs
Bodnar, C., Giovanni, F. D., Chamberlain, B. P., Liò, P., and Bronstein, M. M · 2023
Closest in time.
On over-squashing in message passing neural networks: The impact of width, depth, and topology
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio, P., and Bronstein, M. M · 2023
Closest in time.
Tractable probabilistic graph representation learning with graph-induced sum-product networks
Errica, F. and Niepert, M · 2023
Closest in time.
DRew: Dynamically rewired message passing with delay
Gutteridge, B., Dong, X., Bronstein, M. M., and Di Giovanni, F · 2023
Closest in time.
A generalization of ViT/MLP-mixer to graphs
He, X., Hooi, B., Laurent, T., Perold, A., Lecun, Y., and Bresson, X · 2023
Closest in time.
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Karhadkar, K., Banerjee, P. K., and Montufar, G · 2023
Closest in time.
Graph Inductive Biases in Transformers without Message Passing
Ma, L., Lin, C., Lim, D., Romero-Soriano, A., Dokania, K., Coates, M., H.S. Torr, P., and Lim, S.-N · 2023
Closest in time.
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., and Prokhorenkova, L · 2023
Closest in time.
Exphormer: Sparse transformers for graphs
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K · 2023
Closest in time.
Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing
Tönshoff, J., Ritzert, M., Wolf, H., and Grohe, M · 2023
Closest in time.
Where did the gap go? reassessing the long-range graph benchmark
Tönshoff, J., Ritzert, M., Rosenbluth, E., and Grohe, M · 2023
Closest in time.
Homomorphism counts for graph neural networks: All about that basis
Jin, E., Bronstein, M., Ceylan, İ. İ., and Lanzinger, M · 2024
Closest in time.