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We address two fundamental questions about graph neural networks (GNNs).
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Computational capabilities of graph neural networks
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Spectrally-normalized margin bounds for neural networks
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Deriving neural architectures from sequence and graph kernels
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Generalization Error of Invariant Classifiers
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Size-independent sample complexity of neural networks
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Junction tree variational autoencoder for molecular graph generation
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A property testing framework for the theoretical expressivity of graph kernels
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A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
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Universal invariant and equivariant graph neural networks
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Weisfeiler and leman go neural: Higher-order graph neural networks
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
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Improved generalization bound of permutation invariant deep neural networks
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Approximation ratios of graph neural networks for combinatorial problems
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Stability and generalization of graph convolutional neural networks
S. Verma and Z.-L. Zhang · 2019
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Measuring abstract reasoning in neural networks
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The Vapnik-Chervonenkis dimension of graph and recursive neural networks
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Graph attention networks
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Representation learning on graphs with jumping knowledge networks
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Hierarchical graph representation learning with differentiable pooling
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Can sgd learn recurrent neural networks with provable generalization?
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How powerful are graph neural networks?
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Position-aware graph neural networks
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Graph transformer networks
S. Yun, M. Jeong, R. Kim, J. Kang, and H. Kim · 2019
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The logical expressiveness of graph neural networks
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Directional message passing for molecular graphs
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A survey on graph kernels
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