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Graph Neural Networks (GNN) are inherently limited in their expressive power.
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings, 2019a
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Large batch optimization for deep learning: Training bert in 76 minutes, 2019
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Alchemy: A quantum chemistry dataset for benchmarking ai models, 2019a
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Pytorch: An imperative style, high-performance deep learning library, 2019
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Kombinatorische anzahlbestimmungen für gruppen, graphen und chemische verbindungen
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Topology of series-parallel networks
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An introduction to the theory of numbers
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Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
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Applied combinatorics
Tucker, A · 1994
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A partial k-arboretum of graphs with bounded treewidth
Bodlaender, H. L · 1998
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Algebraic invariants of graphs; a study based on computer exploration
Thiéry, N. M · 2000
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2003
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Principal neighbourhood aggregation for graph nets, 2020
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2004
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Graph Theory (Graduate Texts in Mathematics)
Diestel, R · 2005
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Hierarchical inter-message passing for learning on molecular graphs, 2020
Fey, M., Yuen, J.-G., and Weichert, F · 2006
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Invariants, equivariants and characters in symmetric bifurcation theory
Antoneli, F., Dias, A. P. S., and Matthews, P. C · 2008
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On recognizing graphs by numbers of homomorphisms
Dvořák, Z · 2010
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Large networks and graph limits , volume 60
Lovász, L · 2012
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Graphical enumeration
Harary, F. and Palmer, E. M · 2014
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A new formula for the generating function of the numbers of simple graphs
Bedratyuk, L · 2015
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Tensor network contractions for #SAT
Biamonte, J. D., Morton, J., and Turner, J · 2015
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Computational invariant theory
Derksen, H. and Kemper, G · 2015
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Semi-supervised classification with graph convolutional networks, 2016
Kipf, T. N. and Welling, M · 2016
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Residual gated graph convnets, 2017
Bresson, X. and Laurent, T · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Lov’asz meets weisfeiler and leman
Dell, H., Grohe, M., and Rattan, G · 2018
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Energy flow polynomials: A complete linear basis for jet substructure
Komiske, P. T., Metodiev, E. M., and Thaler, J · 2018
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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On the expressive power of query languages for matrices
Brijder, R., Geerts, F., Bussche, J. V. D., and Weerwag, T · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
On graph neural networks versus graph-augmented mlps
Chen, L., Chen, Z., and Bruna, J · 2021
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Reconstruction for powerful graph representations
Cotta, L., Morris, C., and Ribeiro, B · 2021
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On the expressive power of linear algebra on graphs
Geerts, F · 2021
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Homomorphism tensors and linear equations
Grohe, M., Rattan, G., and Seppelt, T · 2021
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Weisfeiler and leman go machine learning: The story so far
Morris, C., Lipman, Y., Maron, H., Rieck, B., Kriege, N. M., Grohe, M., Fey, M., and Borgwardt, K · 2021
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Random features strengthen graph neural networks
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A simple proof of the universality of invariant/equivariant graph neural networks
Maehara, T. and NT, H · 2019
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Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
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Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
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Approximation ratios of graph neural networks for combinatorial problems
Sato, R., Yamada, M., and Kashima, H · 2019
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On universal equivariant set networks
Segol, N. and Lipman, Y · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Sato, R., Yamada, M., and Kashima, H · 2021
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Autobahn: Automorphism-based graph neural nets
Thiede, E., Zhou, W., and Kondor, R · 2021
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Identity-aware graph neural networks
You, J., Gomes-Selman, J. M., Ying, R., and Leskovec, J · 2021
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Nested graph neural networks
Zhang, M. and Li, P · 2021
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From stars to subgraphs: Uplifting any gnn with local structure awareness, 2021
Zhao, L., Jin, W., Akoglu, L., and Shah, N · 2021
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Equivariant subgraph aggregation networks
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M. M., and Maron, H · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G., Frasca, F., Zafeiriou, S., and Bronstein, M. M · 2022
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PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Dupty, M. H., Dong, Y., and Lee, W. S · 2022
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Understanding and extending subgraph GNNs by rethinking their symmetries
Frasca, F., Bevilacqua, B., Bronstein, M. M., and Maron, H · 2022
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Expressiveness and approximation properties of graph neural networks
Geerts, F. and Reutter, J. L · 2022
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Sign and basis invariant networks for spectral graph representation learning
Lim, D., Robinson, J., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S · 2022
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Speqnets: Sparsity-aware permutation-equivariant graph networks, 2022
Morris, C., Rattan, G., Kiefer, S., and Ravanbakhsh, S · 2022
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Frame averaging for invariant and equivariant network design
Puny, O., Atzmon, M., Smith, E. J., Misra, I., Grover, A., Ben-Hamu, H., and Lipman, Y · 2022
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Ordered subgraph aggregation networks
Qian, C., Rattan, G., Geerts, F., Niepert, M., and Morris, C · 2022
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Expectation complete graph representations using graph homomorphisms
Welke, P., Thiessen, M., and Gärtner, T · 2022
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A practical, progressively-expressive GNN
Zhao, L., Shah, N., and Akoglu, L · 2022
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Recipe for a general, powerful, scalable graph transformer, 2023
Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2023
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Rethinking the expressive power of gnns via graph biconnectivity
Zhang, B., Luo, S., Wang, L., and Di, H · 2023
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