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Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during which parameterized transformation matrices should be learned for the node feature updating.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
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Hierarchical graph representation learning with differentiable pooling
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Learning deep representations by mutual information estimation and maximization
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