Graph neural networks (GNNs) have been widely adopted for modeling graph-structure data. Most existing GNN studies have focused on designing different strategies to propagate information over the graph structures. After systematic investigations. we observe that the propagation step in GNNs matters. https://www.roneverhart.com/HGST-Ultrastar-7K6000-HUS726040ALS214-4TB-7-2K-RPM-SAS-12Gb-s-512n-128MB-3-5-SE-Hard-Drive/
On the distribution alignment of propagation in graph neural networks
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