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The ability of Graph Neural Networks (GNNs) to capture long-range and global topology information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs.
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Complete the missing half: Augmenting aggregation filtering with diversification for graph convolutional networks
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