XGCN：大规模图神经网络推荐库

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2024-03-15 DOI:10.1007/s11704-024-3803-z

Xiran Song, Hong Huang, Jianxun Lian, Hai Jin

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引用次数: 0

摘要

本作品介绍了一个 GNN 库 XGCN，旨在帮助用户快速开发和运行大规模 GNN 推荐模型。我们提供了高度可扩展的 GNN 复制品，其中包括最近提出的 GNN 模型：xGCN。在不同规模的数据集上进行的实验评估表明，我们的 XGCN 库具有卓越的可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

This work introduces a GNN library, XGCN, which is designed to assist users in rapidly developing and running large-scale GNN recommendation models. We offer highly scalable GNN reproductions and include a recently proposed GNN model: xGCN. Experimental evaluations on datasets of varying scales demonstrate the superior scalability of our XGCN library.

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.