arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2102.07933cs.AIcs.LGcs.SE
收藏

GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

Sun Yat-sen University(中山大学);Deakin University(迪肯大学)

Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, Xiao Liu

英文摘要:

Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy development of GNNs based software. GraphGallery is an easy-to-use platform that allows developers to automatically deploy GNNs even with less domain-specific knowledge. It offers a set of implementations of common GNN models based on mainstream deep learning frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be easily incorporated into the platform. Experiments demonstrate the reliability of implementations and superiority in fast coding. The official source code of GraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a demo video can be found at https://youtu.be/mv7Zs1YeaYo.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑