Training a two-hidden-layer GCN with jumping connections and layer-wise edge pruning nearly matches the best achievable error in a class of hierarchical target functions, with shallow layers needing more conservative sparsification than deep layers.
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Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
Training a two-hidden-layer GCN with jumping connections and layer-wise edge pruning nearly matches the best achievable error in a class of hierarchical target functions, with shallow layers needing more conservative sparsification than deep layers.