Fetching the paper…
Reading the bibliography…
Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between $k$-tuples of vertices.
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Morris, C., Rattan, G., and Mutzel, P · 1904
Earlier work this paper cites.
Are powerful graph neural nets necessary? A dissection on graph classification
Chen, T., Bian, S., and Sun, Y · 1905
Earlier work this paper cites.
A hierarchy of graph neural networks based on learnable local features
Li, M. L., Dong, M., Zhou, J., and Rush, A. M · 1911
Earlier work this paper cites.
Operations with structures
Lovász, L · 1967
Earlier work this paper cites.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Weisfeiler, B. J. and Lehman, A. A · 1968
Earlier work this paper cites.
An optimal lower bound on the number of variables for graph identifications
Cai, J., Fürer, M., and Immerman, N · 1992
Earlier work this paper cites.
Logical hierarchies in PTIME
Hella, L · 1996
Earlier work this paper cites.
Can graph neural networks count substructures?
Chen, Z., Chen, L., Villar, S., and Bruna, J · 2002
Earlier work this paper cites.
Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
Belkin, M. and Niyogi, P · 2003
Earlier work this paper cites.
Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2003
Earlier work this paper cites.
A survey on the expressive power of graph neural networks
Sato, R · 2003
Earlier work this paper cites.
Let’s agree to degree: Comparing graph convolutional networks in the message-passing framework
Geerts, F., Mazowiecki, F., and Pérez, G. A · 2004
Earlier work this paper cites.
Machine learning on graphs: A model and comprehensive taxonomy
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., and Murphy, K · 2005
Earlier work this paper cites.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 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.
Graph homomorphism convolution
NT, H. and Maehara, T · 2005
Earlier work this paper cites.
Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G., Frasca, F., Zafeiriou, S., and Bronstein, M. M · 2006
Earlier work this paper cites.
Weisfeiler-Lehman embedding for molecular graph neural networks
Ishiguro, K., Oono, K., and Hayashi, K · 2006
Earlier work this paper cites.
On the power of k k -consistency
Atserias, A., Bulatov, A. A., and Dalmau, V · 2007
Cited alongside, same era.
A novel higher-order Weisfeiler-Lehman graph convolution
Damke, C., Melnikov, V., and Hüllermeier, E · 2007
Cited alongside, same era.
The expressive power of k k th-order invariant graph networks
Geerts, F · 2007
Cited alongside, same era.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
Cited alongside, same era.
Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009
Cited alongside, same era.
The surprising power of graph neural networks with random node initialization
Lovász meets Weisfeiler and Leman
Dell, H., Grohe, M., and Rattan, G · 2018
Later among the works it cites.
Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Later among the works it cites.
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M · 2018
Later among the works it cites.
How many variables are needed to express an existential positive query?
Bova, S. and Chen, H · 2019
Later among the works it cites.
On the equivalence between graph isomorphism testing and function approximation with GNNs
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
Later among the works it cites.
Universal invariant and equivariant graph neural networks
Keriven, N. and Peyré, G · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Abboud, R., Ceylan, İ. İ., Grohe, M., and Lukasiewicz, T · 2010
Cited alongside, same era.
On recognizing graphs by numbers of homomorphisms
Dvorak, Z · 2010
Cited alongside, same era.
Tahmasebi, B. and Jegelka, S · 2012
Cited alongside, same era.
Bresson, X. and Laurent, T · 2017
Cited alongside, same era.
Homomorphisms are a good basis for counting small subgraphs
Curticapean, R., Dell, H., and Marx, D · 2017
Cited alongside, same era.
On the combinatorial power of the Weisfeiler-Lehman algorithm
Fürer, M · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Later among the works it cites.
Quantifying the carbon emissions of machine learning
Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T · 2019
Later among the works it 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
Later among the works it cites.
Approximation ratios of graph neural networks for combinatorial problems
Sato, R., Yamada, M., and Kashima, H · 2019
Later among the works it cites.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Later among the works it cites.
On Weisfeiler-Leman invariance: Subgraph counts and related graph properties
Arvind, V., Fuhlbrück, F., Köbler, J., and Verbitsky, O · 2020
Later among the works it cites.
The logical expressiveness of graph neural networks
Barceló, P., Kostylev, E. V., Monet, M., Pérez, J., Reutter, J., and Silva, J. P · 2020
Later among the works it cites.
Generalization and representational limits of graph neural networks
Garg, V. K., Jegelka, S., and Jaakkola, T. S · 2020
Later among the works it cites.
What graph neural networks cannot learn: depth vs width
Loukas, A · 2020
Later among the works it cites.
Building powerful and equivariant graph neural networks with structural message-passing
Vignac, C., Loukas, A., and Frossard, P · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2020
Later among the works it cites.
Distributed subgraph counting: A general approach
Zhang, H., Yu, J. X., Zhang, Y., Zhao, K., and Cheng, H · 2020
Later among the works it cites.
Expressive power of invariant and equivariant graph neural networks
Azizian, W. and Lelarge, M · 2021
Closest in time.