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Decoupling the Depth and Scope of Graph Neural Networks
[article]
2022
arXiv
pre-print
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
arXiv:2201.07858v1
fatcat:lzoilhqdrnbefntai4onjjoie4