  State-of-the-art Graph Neural Networks (GNNs) have limited scalability with
respect to the graph and model sizes. On large graphs, increasing the model
depth often means exponential expansion of the scope (i.e., receptive field).
Beyond just a few layers, two fundamental challenges emerge: 1. degraded
expressivity due to oversmoothing, and 2. expensive computation due to
neighborhood explosion. We propose a design principle to decouple the depth and
scope of GNNs -- to generate representation of a target entity (i.e., a node or
an edge), we first extract a localized subgraph as the bounded-size scope, and
then apply a GNN of arbitrary depth on top of the subgraph. A properly
extracted subgraph consists of a small number of critical neighbors, while
excluding irrelevant ones. The GNN, no matter how deep it is, smooths the local
neighborhood into informative representation rather than oversmoothing the
global graph into "white noise". Theoretically, decoupling improves the GNN
expressive power from the perspectives of graph signal processing (GCN),
function approximation (GraphSAGE) and topological learning (GIN). Empirically,
on seven graphs (with up to 110M nodes) and six backbone GNN architectures, our
design achieves significant accuracy improvement with orders of magnitude
reduction in computation and hardware cost.
