[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127127-en":3,"doc-seo-127127-105":31,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127127,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","GraphStorm - all-in-one graph machine learning framework for industry applications","Graph machine learning (GML) delivers strong results in business settings, yet adoption remains difficult due to the effort required for scalable graph construction, training, and inference on massive, complex industry datasets. GraphStorm provides an end-to-end solution that streamlines these steps while balancing usability, flexibility, modeling capability, training efficiency, and scalability. It supports single-command execution, incorporates advanced modeling techniques for complex heterogeneous graph data, and scales to graphs with billions of nodes across different hardware without code changes.","GraphStorm: all-in-one graph machine learning framework for  \nindustry applications  \nDa 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,  \nChristos Faloutsos, George Karypis  \nAmazon  \nUSA  \n{dzzhen, xiangsx, qzhuamzn, jamezhan, thvasilo, runjie, zhanhouy, zichewan, adesojia, nisisrat, amottini, qingjunc, rhuzefa, zengb, faloutso, [gkarypis}@amazon.com](gkarypis}@amazon.com)  \narXiv :2406 .06022v1 [ cs .LG] 10 Jun 2024  \nABSTRACT  \nGraph 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](https://github.com/awslabs/graphstorm).  \nACM Reference Format:  \nDa 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. 2024. GraphStorm: all-in-one graph machine learning framework for industry applications. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’24), August 25–29, 2024, Barcelona, Spain. ACM, New York, NY, USA, 13 pages. [https://doi.org/10](https://doi.org/10) . 1145/3637528.3671603  \n1 INTRODUCTION  \nRecent research has demonstrated the value of GML across a range of applications and domains, such as social networks and e-commerce. However, deploying such GML solutions to solve real business problems remains challenging for three reasons. First, industry graphs are massive, usually in the order of many millions or even billions of nodes and edges. Second, industry graphs are complex. They are usually heterogeneous with multiple node types and edge types.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nKDD’24, August 25–29, 2024, Barcelona, Spain  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-0490-1/24/08  \n[https://doi.org/10.1145/3637528.3671603](https://doi.org/10.1145/3637528.3671603)  \nFigure 1: Easy and Scalable GML with GraphStorm.  \nSome nodes and edges are associated with diverse features, such as numerical, categorical and text/image features, while some other nodes or edges have no features. Third, many applications do not store data in a graph format. To apply GML to these data, we need to first construct a graph. Defining a graph schema is part of graph modeling and often requires multiple rounds of t","cbCaid6td9lUKwwL","https://ap.wps.com/l/cbCaid6td9lUKwwL","pdf",1662610,2,1,13,"English","en",105,"# Introduction\n## Challenges of applying GML in industry\n## GraphStorm: end-to-end framework overview","[{\"question\":\"How does GraphStorm achieve scalability for very large graphs?\",\"answer\":\"GraphStorm scales components to graphs with billions of nodes and supports scaling training and inference across different hardware without changing code. For even larger graphs, it builds on the distributed GNN system DistDGL and provides efficient implementations of scalable algorithms.\"}]","GraphStorm - all-in-one graph machine learning framework for industry applications | PDF",1785937020,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"graphstorm-all-in-one-graph-machine-learning-framework-for-industry-applications","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/graphstorm-all-in-one-graph-machine-learning-framework-for-industry-applications/127127/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"How does GraphStorm achieve scalability for very large graphs?","Question",{"text":76,"@type":77},"GraphStorm scales components to graphs with billions of nodes and supports scaling training and inference across different hardware without changing code. 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