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Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another.
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Inductive representation learning on large graphs
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
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As-gcn: Adaptive semantic architecture of graph convolutional networks for text-rich networks
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Interpreting and unifying graph neural networks with an optimization framework
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Block modeling-guided graph convolutional neural networks
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Powerful graph convolutioal networks with adaptive propagation mechanism for homophily and heterophily
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