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Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs).
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Your transformer may not be as powerful as you expect
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A theoretical comparison of graph neural network extensions
P. A. Papp and R. Wattenhofer · 2022
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Equivariant polynomials for graph neural networks
O. Puny, D. Lim, B. T. Kiani, H. Maron, and Y. Lipman · 2023
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Rethinking the expressive power of gnns via graph biconnectivity
B. Zhang, S. Luo, D. He, and L. Wang · 2023
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