GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications

Da Zheng; Xiang Song; Qi Zhu; Jian Zhang; Theodore Vasiloudis; Runjie Ma; Houyu Zhang; Zichen Wang; Soji Adeshina; Israt Nisa; Alejandro Mottini; Qingjun Cui; Huzefa Rangwala; Belinda Zeng; Christos Faloutsos; George Karypis

doi:10.1145/3637528.3671603

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications

Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	6356-6367
Number of pages	12
ISBN (Electronic)	9798400704901
DOIs	https://doi.org/10.1145/3637528.3671603
State	Published - 25 Aug 2024
Externally published	Yes
Event	30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 - Barcelona, Spain Duration: 25 Aug 2024 → 29 Aug 2024

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)	2154-817X

Conference

Conference	30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024
Country/Territory	Spain
City	Barcelona
Period	25/08/24 → 29/08/24

Keywords

graph machine learning
industry scale

Access to Document

10.1145/3637528.3671603

Cite this

Zheng, D., Song, X., Zhu, Q., Zhang, J., Vasiloudis, T., Ma, R., Zhang, H., Wang, Z., Adeshina, S., Nisa, I., Mottini, A., Cui, Q., Rangwala, H., Zeng, B., Faloutsos, C., & Karypis, G. (2024). GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. In KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6356-6367). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3637528.3671603

Zheng, Da ; Song, Xiang ; Zhu, Qi et al. / GraphStorm : All-in-one Graph Machine Learning Framework for Industry Applications. KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2024. pp. 6356-6367 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{56b3e768afd14a6fa0970893ce76b0fb,

title = "GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications",

abstract = "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.",

keywords = "graph machine learning, industry scale",

author = "Da Zheng and Xiang Song and Qi Zhu and Jian Zhang and Theodore Vasiloudis and Runjie Ma and Houyu Zhang and Zichen Wang and Soji Adeshina and Israt Nisa and Alejandro Mottini and Qingjun Cui and Huzefa Rangwala and Belinda Zeng and Christos Faloutsos and George Karypis",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024 ; Conference date: 25-08-2024 Through 29-08-2024",

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doi = "10.1145/3637528.3671603",

language = "English",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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Zheng, D, Song, X, Zhu, Q, Zhang, J, Vasiloudis, T, Ma, R, Zhang, H, Wang, Z, Adeshina, S, Nisa, I, Mottini, A, Cui, Q, Rangwala, H, Zeng, B, Faloutsos, C & Karypis, G 2024, GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. in KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 6356-6367, 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2024, Barcelona, Spain, 25/08/24. https://doi.org/10.1145/3637528.3671603

GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. / Zheng, Da; Song, Xiang; Zhu, Qi et al.
KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2024. p. 6356-6367 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zheng, Da

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AU - Vasiloudis, Theodore

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AU - Zhang, Houyu

AU - Wang, Zichen

AU - Adeshina, Soji

AU - Nisa, Israt

AU - Mottini, Alejandro

AU - Cui, Qingjun

AU - Rangwala, Huzefa

AU - Zeng, Belinda

AU - Faloutsos, Christos

AU - Karypis, George

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N2 - 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.

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Zheng D, Song X, Zhu Q, Zhang J, Vasiloudis T, Ma R et al. GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications. In KDD 2024 - Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2024. p. 6356-6367. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3637528.3671603