GraphStorm: all-in-one graph machine learning framework for industry applications
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911911769014272 |
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| author | Zheng, Da Song, Xiang Zhu, Qi Zhang, Jian Vasiloudis, Theodore Ma, Runjie Zhang, Houyu Wang, Zichen Adeshina, Soji Nisa, Israt Mottini, Alejandro Cui, Qingjun Rangwala, Huzefa Zeng, Belinda Faloutsos, Christos Karypis, George |
| author_facet | Zheng, Da Song, Xiang Zhu, Qi Zhang, Jian Vasiloudis, Theodore Ma, Runjie Zhang, Houyu Wang, Zichen Adeshina, Soji Nisa, Israt Mottini, Alejandro Cui, Qingjun Rangwala, Huzefa Zeng, Belinda Faloutsos, Christos Karypis, George |
| contents | Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06022 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphStorm: all-in-one graph machine learning framework for industry applications Zheng, Da Song, Xiang Zhu, Qi Zhang, Jian Vasiloudis, Theodore Ma, Runjie Zhang, Houyu Wang, Zichen Adeshina, Soji Nisa, Israt Mottini, Alejandro Cui, Qingjun Rangwala, Huzefa Zeng, Belinda Faloutsos, Christos Karypis, George Machine Learning Distributed, Parallel, and Cluster Computing Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm. |
| title | GraphStorm: all-in-one graph machine learning framework for industry applications |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.06022 |