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HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models
Graph neural networks typically propagate information through repeated message-passing layers, coupling propagation distance with the number of nonlinear transformations applied. This coupling can make deep architectures difficult to optimize and lead to over-smoothing, over-squashing, and loss of l
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