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Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
A fast and high quality multilevel scheme for partitioning irregular graphs
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Combining label propagation and simple models out-performs graph neural networks
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Unifying graph convolutional neural networks and label propagation
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Graph neural networks exponentially lose expressive power for node classification
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Vq-gnn: A universal framework to scale up graph neural networks using vector quantization
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Neighbor2seq: Deep learning on massive graphs by transforming neighbors to sequences
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