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Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently.
Fast graph representation learning with pytorch geometric
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Learning theory can (sometimes) explain generalisation in graph neural networks
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A pac-bayesian approach to generalization bounds for graph neural networks
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
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Deep equilibrium models
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From local structures to size generalization in graph neural networks
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The generalization error of graph convolutional networks may enlarge with more layers
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
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Optimization-induced graph implicit nonlinear diffusion
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p-laplacian based graph neural networks
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Graph neural networks as gradient flows
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Not too little, not too much: a theoretical analysis of graph (over)smoothing
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On neural differential equations
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Generalization guarantee of training graph convolutional networks with graph topology sampling
Li, H., Wang, M., Liu, S., Chen, P., and Xiong, J. (2022) · 2022
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MGNNI: multiscale graph neural networks with implicit layers
Liu, J., Hooi, B., Kawaguchi, K., and Xiao, X. (2022) · 2022
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Generalization analysis of message passing neural networks on large random graphs
Maskey, S., Levie, R., Lee, Y., and Kutyniok, G. (2022) · 2022
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Convergent graph solvers
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GRAND++: graph neural diffusion with A source term
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