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

作　　者：Xiran SONG Hong HUANG Jianxun LIAN Hai JIN 

机构地区：[1]National Engineering Research Center for Big Data Technology and System,Services Computing Technology and System Lab,Cluster and Grid Computing Lab,School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China [2]Microsoft Research Asia,Beijing 100080,China

出　　处：《Frontiers of Computer Science》2024年第3期247-249,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62172174,61932004).

摘　　要：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.

关 键 词：SCALING gained operations 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]