Fetching the paper…
Reading the bibliography…
The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
The reduction of a graph to canonical form and the algebra which appears therein
Weisfeiler, B. and Leman, A · 1968
Earlier work this paper cites.
The graph isomorphism disease
Read, R. C. and Corneil, D. G · 1977
Earlier work this paper cites.
Quasicrystals: a new class of ordered structures
Levine, D. and Steinhardt, P. J · 1984
Earlier work this paper cites.
Transforming auto-encoders
Hinton, G., Krizhevsky, A., and Wang, S. D · 2011
Earlier work this paper cites.
On representing chemical environments
Bartók, A. P., Kondor, R., and Csányi, G · 2013
Earlier work this paper cites.
Topology and geometry
Bredon, G. E · 2013
Earlier work this paper cites.
Smooth manifolds
Lee, J. M · 2013
Earlier work this paper cites.
Moment tensor potentials: A class of systematically improvable interatomic potentials
Shapeev, A. V · 2016
Earlier work this paper cites.
Group theory in a nutshell for physicists
Zee, A · 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.
Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R · 2018
Earlier work this paper cites.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Earlier work this paper cites.
3d steerable cnns: Learning rotationally equivariant features in volumetric data
Weiler, M., Geiger, M., Welling, M., Boomsma, W., and Cohen, T. S · 2018
Earlier work this paper cites.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C · 2018
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S., and Kondor, R · 2019
Earlier work this paper cites.
On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
Earlier work this paper cites.
Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R · 2019
Earlier work this paper cites.
Atomic cluster expansion: Completeness, efficiency and stability
Dusson, G., Bachmayr, M., Csanyi, G., Drautz, R., Etter, S., van der Oord, C., and Ortner, C · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Earlier work this paper cites.
Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
Cited alongside, same era.
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.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
Cited alongside, same era.
On the universality of rotation equivariant point cloud networks
Dym, N. and Maron, H · 2020
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V., and Welling, M · 2020
Cited alongside, same era.
E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Later among the works it cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O., and Gastegger, M · 2021
Later among the works it cites.
Scalars are universal: Equivariant machine learning, structured like classical physics
Villar, S., Hogg, D. W., Storey-Fisher, K., Yao, W., and Blum-Smith, B · 2021
Later among the works it cites.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
Later among the works it cites.
Symmetry group equivariant architectures for physics
Bogatskiy, A., Ganguly, S., Kipf, T., Kondor, R., Miller, D. W., Murnane, D., Offermann, J. T., Pettee, M., Shanahan, P., Shimmin, C., et al · 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…
Generalization and representational limits of graph neural networks
Garg, V., Jegelka, S., and Jaakkola, T · 2020
Cited alongside, same era.
Directional message passing for molecular graphs
Gasteiger, J., Groß, J., and Günnemann, S · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., and Dror, R · 2020
Cited alongside, same era.
Transformers are graph neural networks
Joshi, C · 2020
Cited alongside, same era.
Graph neural networks for decentralized multi-robot path planning
Li, Q., Gama, F., Ribeiro, A., and Prorok, A · 2020
Cited alongside, same era.
Incompleteness of atomic structure representations
Pozdnyakov, S. N., Willatt, M. J., Bartók, A. P., Ortner, C., Csányi, G., and Ceriotti, M · 2020
Cited alongside, same era.
Geometric and physical quantities improve e(3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E. J., and Welling, M · 2022
Later among the works it cites.
Robust deep learning based protein sequence design using proteinmpnn
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I., Courbet, A., de Haas, R. J., Bethel, N., et al · 2022
Later among the works it cites.
Se (3) equivariant graph neural networks with complete local frames
Du, W., Zhang, H., Du, Y., Meng, Q., Chen, W., Zheng, N., Shao, B., and Liu, T.-Y · 2022
Later among the works it cites.
e3nn: Euclidean neural networks
Geiger, M. and Smidt, T · 2022
Later among the works it cites.
Graphein - a python library for geometric deep learning and network analysis on biomolecular structures and interaction networks
Jamasb, A. R., Torné, R. V., Ma, E. J., Du, Y., Harris, C., Huang, K., Hall, D., Lio, P., and Blundell, T. L · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs
Liu, Y., Wang, L., Liu, M., Lin, Y., Zhang, X., Oztekin, B., and Ji, S · 2022
Later among the works it cites.
Incompleteness of graph convolutional neural networks for points clouds in three dimensions
Pozdnyakov, S. N. and Ceriotti, M · 2022
Later among the works it cites.
Benchmarking graphormer on large-scale molecular modeling datasets
Shi, Y., Zheng, S., Ke, G., Shen, Y., You, J., He, J., Luo, S., Liu, C., He, D., and Liu, T.-Y · 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.
Comenet: Towards complete and efficient message passing for 3d molecular graphs
Wang, L., Liu, Y., Lin, Y., Liu, H., and Ji, S · 2022
Later among the works it cites.
A hitchhiker’s guide to geometric gnns for 3d atomic systems
Duval, A., Mathis, S. V., Joshi, C. K., Schmidt, V., Miret, S., Malliaros, F. D., Cohen, T., Liò, P., Bengio, Y., and Bronstein, M · 2023
Closest in time.
Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2023
Closest in time.
Learning hierarchical protein representations via complete 3d graph networks
Wang, L., Liu, H., Liu, Y., Kurtin, J., and Ji, S · 2023
Closest in time.