Graph neural networks (GNNs) have gained widespread adoption in recommendation systems. When it comes to processing large graphs, GNNs may encounter the scalability issue stemming from their multi-layer message-passing operations. Consequently, scaling GNNs has emerged as a crucial research area in recent years, with numerous scaling strategies being proposed.
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