Graph-Learn(GL) is a framework designed to simplify the application of graph neural networks(GNNs). It abstracts solutions from the actual production cases. These solutions have been applied and verified on recommendation, anti-cheating and knowledge graph systems.
GL is portable and flexible, which makes it much more friendly to developers. Based on GL, developers are able to implement a kind of GNNs algorithms, customize some graph related operators and extend the existed modules easily. GL can be installed in containers or on physical machines, and deployed in single machine mode or distributed mode.
Please cite the following paper in your publications if GL helps your research.
@article{zhu2019aligraph,
title={AliGraph: a comprehensive graph neural network platform},
author={Zhu, Rong and Zhao, Kun and Yang, Hongxia and Lin, Wei and Zhou, Chang and Ai, Baole and Li, Yong and Zhou, Jingren},
journal={Proceedings of the VLDB Endowment},
volume={12},
number={12},
pages={2094--2105},
year={2019},
publisher={VLDB Endowment}
}
Apache License 2.0.
The developers of GL are from several teams at Alibaba, including Computing Platform Department - PAI team, New Retail Intelligence Engine - Data Analytics And Intelligence Lab, and Security Department - Data and Algorithms team. Thanks to the ones who provide helps and suggestions to open source.
Please email [email protected] if any questions.
Welcome to contribution!
GL refers to the following projects. Thanks to the authors and contributors.