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Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness.
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The reduction of a graph to canonical form and the algebra which appears therein
Weisfeiler, B. and Leman, A · 1968
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Canonical labelling of graphs in linear average time
Babai, L. and Kucera, L · 1979
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An optimal lower bound on the number of variables for graph identification
Cai, J.-Y., Furer, M., and Immerman, N · 1989
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Statistical methods for research workers
Fisher, R. A · 1992
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The generalization of student’s ratio
Hotelling, H · 1992
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Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
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Applied multivariate statistical analysis
Johnson, R. A. and Wichern, D. W · 2007
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Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., and Loscalzo, J · 2011
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Strongly regular graphs
Brouwer, A. E., Haemers, W. H., Brouwer, A. E., and Haemers, W. H · 2012
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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
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Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., Salakhutdinov, R., et al · 2015
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Graph convolutional matrix completion
Berg, R. v. d., Kipf, T. N., and Welling, M · 2017
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On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
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Graph neural networks for social recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., and Yin, D · 2019
Cited alongside, same era.
Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., 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.
Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 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.
Can graph neural networks count substructures?
Chen, Z., Chen, L., Villar, S., and Bruna, J · 2020
Cited alongside, same era.
Dropgnn: Random dropouts increase the expressiveness of graph neural networks
Papp, P. A., Martinkus, K., Faber, L., and Wattenhofer, R · 2021
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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.-Y · 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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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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Distance encoding: Design provably more powerful neural networks for graph representation learning
Li, P., Wang, Y., Wang, H., and Leskovec, J · 2020
Cited alongside, same era.
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Morris, C., Rattan, G., and Mutzel, P · 2020
Cited alongside, same era.
A survey on the expressive power of graph neural networks
Sato, R · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 2020
Cited alongside, same era.
The surprising power of graph neural networks with random node initialization
Abboud, R., Ceylan, İ. İ., Grohe, M., and Lukasiewicz, T · 2021
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Breaking the limits of message passing graph neural networks
Balcilar, M., Héroux, P., Gauzere, B., Vasseur, P., Adam, S., and Honeine, P · 2021
Cited alongside, same era.
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How powerful are k-hop message passing graph neural networks
Feng, J., Chen, Y., Li, F., Sarkar, A., and Zhang, M · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Frasca, F., Bevilacqua, B., Bronstein, 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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A theoretical comparison of graph neural network extensions
Papp, P. A. and Wattenhofer, R · 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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Strongly regular graphs satisfying the 4-vertex condition
Brouwer, A. E., Ihringer, F., and Kantor, W. M · 2023
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Boosting the cycle counting power of graph neural networks with i$ˆ2$-gnns
Huang, Y., Peng, X., Ma, J., and Zhang, M · 2023
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Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness, 2024
Zhang, B., Gai, J., Du, Y., Ye, Q., He, D., and Wang, L · 2024
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