[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125657-en":3,"doc-seo-125657-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125657,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Towards Data-centric Graph Machine Learning: Review and Outlook","Data-centric AI centers on collecting, managing, and using data to improve machine learning models and applications. This article delivers an in-depth review and forward-looking outlook on data-centric efforts for graph data, which captures complex dependencies among massive real-world entities. It proposes a structured framework, Data-centric Graph Machine Learning (DC-GML), covering the full graph data lifecycle—collection, exploration, improvement, exploitation, and maintenance—supported by a taxonomy addressing availability/quality, learning under limited-quality data, and graph MLOps construction.","arXiv :2309 . 10979v1 [ cs .LG] 20 Sep 2023  \nTowards Data-centric Graph Machine Learning: Review and Outlook  \nXIN ZHENG∗ and YIXIN LIU∗ , Monash University, Australia  \nZHIFENG BAO, RMIT University, Australia MENG FANG, University of Liverpool, UK XIA HU, Rice University, US  \nALAN WEE-CHUNG LIEW and SHIRUI PAN†, Griffith University, Australia  \nData-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an indepth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data—the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities. We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages ofthe graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance. A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view. Lastly, we pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications 1 .  \nCCS Concepts: • Computing methodologies → Machine learning; • Information systems → Data  \nmining; Network data models.  \nAdditional Key Words and Phrases: data-centric AI, graphs, machine learning, graph neural networks, MLOps ACM Reference Format:  \nXin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, and Shirui Pan. 2023. Towards Data-centric Graph Machine Learning: Review and Outlook. J. ACM 37, 4, Article 111 (September 2023), 42 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 INTRODUCTION  \nWith the enormous growth of data, the advances and potentials of artificial intelligence (AI) have been explored and developed remarkably, creating ample opportunities for development in the research domain of machine learning (ML) . Over the years, researchers have dedicated their efforts to the development of model-centric AI, which has become a central focus in both domains of ML  \n∗ Both authors contributed equally to this research.  \n†Correspoinding Author.  \n1 Github Page: [https://github.com/Data-Centric-GraphML/awesome-papers](https://github.com/Data-Centric-GraphML/awesome-papers)  \nAuthors’ addresses: Xin Zheng, [xin.zheng@monash.edu](xin.zheng@monash.edu); Yixin Liu, [yixin.liu@monash.edu](yixin.liu@monash.edu), Monash University, Melbourne,  \nVIC, Australia, 3800; Zhifeng Bao, RMIT University, Melbourne, VIC, Australia, [zhifeng.bao@rmit.edu.au](zhifeng.bao@rmit.edu.au); Meng Fang, [Meng.Fang@liverpool.ac.uk](Meng.Fang@liverpool.ac.uk), University of Liverpool, Liverpool, UK; Xia Hu, [xia.hu@rice.edu](xia.hu@rice.edu), Rice University, Houston, US; Alan Wee-Chung Liew, a.liew@griffith.edu.au; Shirui Pan, s.pan@griffith.edu.au, Griffith University, Gold Coast, Queensland, Australia.  \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 ACM 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](prior specific permission and/or a fee. Request permissions from permissions@acm.org).  \n© 2023 Association for Computing Machinery.  \n0004-5411/2023/9-ART111 ","cbCaind4x3Qz5cja","https://ap.wps.com/l/cbCaind4x3Qz5cja","pdf",3351603,1,42,"English","en",105,"# Introduction\n## Data-centric AI vs. model-centric AI\n## Graph data and the DC-GML framework","[{\"question\":\"What is the main focus of data-centric AI in this article?\",\"answer\":\"It emphasizes collecting, managing, and utilizing data with high availability and quality to drive and improve model-related machine learning tasks.\"},{\"question\":\"What does the DC-GML framework cover?\",\"answer\":\"DC-GML spans the entire graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance.\"},{\"question\":\"How does the article characterize key challenges for graph-centric learning?\",\"answer\":\"It targets how to enhance graph data availability and quality, how to learn from graph data under limited availability and low quality, and how to build graph MLOps from a graph data-centric view.\"}]","Towards Data-centric Graph Machine Learning: Review and Outlook | 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is the main focus of data-centric AI in this article?","Question",{"text":75,"@type":76},"It emphasizes collecting, managing, and utilizing data with high availability and quality to drive and improve model-related machine learning tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the DC-GML framework cover?",{"text":80,"@type":76},"DC-GML spans the entire graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the article characterize key challenges for graph-centric learning?",{"text":84,"@type":76},"It targets how to enhance graph data availability and quality, how to learn from graph data under limited availability and low quality, and how to build graph MLOps from a graph data-centric 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