XGCN:a library for large-scale graph neural network recommendations

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing operations.Consequently,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.