[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117146-en":3,"doc-seo-117146-105":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},117146,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Curriculum Graph Machine Learning - A Survey","Graph machine learning has advanced rapidly in academia and industry, yet many existing models train on graph samples in random order. This can be detrimental because it overlooks how sample importance and training order influence optimization and final model quality. Curriculum graph machine learning (Graph CL) merges graph learning with curriculum learning to address this issue. This survey reviews key challenges, formal problem definition, method taxonomy across node-, link-, and graph-level tasks, and outlines future research directions.","Curriculum Graph Machine Learning: A Survey  \nHaoyang Li∗ , Xin Wang∗ , Wenwu Zhu  \nTsinghua University  \n[lihy18@mails.tsinghua.edu.cn](lihy18@mails.tsinghua.edu.cn), {xin wang, [wwzhu](wwzhu}@tsinghua.edu.cn)[}](wwzhu}@tsinghua.edu.cn)[@tsinghua.edu.cn](wwzhu}@tsinghua.edu.cn)  \narXiv :2302 .02926v2 [ cs .LG] 12 Mar 2024  \nAbstract  \nGraph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction.  \nSpecifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, linklevel, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.  \n1 Introduction  \nGraph structured data is ubiquitous in the real world, which has been widely used to model the complex relationships and dependencies among various entities. In the past decade, graph machine learning approaches, especially graph neural networks (GNNs) [Kipf and Welling, 2017; Velikovi et al., 2018; Hamilton et al., 2017], have drawn ever-increasing attention in both academia and industry, which make great progress in a variety of applications across wide-ranging domains, ranging from physics [Sanchez-Gonzalez et al., 2020] to chemistry [Gilmer et al., 2017], and from neuroscience [de Vico Fallani et al., 2014] to social science [Zhang and Tong, 2016] . Other areas, such as recommender systems [Wu et al., 2022], knowledge graphs [Wang et al., 2017a], molecular  \n∗Equal contributions  \nprediction [Hu et al., 2020a], medical detection [Horry et al., 2020], drug repurposing [Hsieh et al., 2021] etc., also provide an increasing demand for the applications of graph machine learning.  \nDespite the popularity of graph machine learning approaches, the existing literature generally trains graph models by feeding the data samples in a random order during the training process. For example, when training GNNs, the widely adopted mini-batch stochastic gradient descent optimization strategy as well as its variants select the data samples in each mini-batch randomly. However, such training strategies largely ignore the importance of different graph data samples and how their orders can affect the optimization status, which may result in suboptimal performance of the graph learning models [Wei et al., 2022; Wang et al., 2021b] . Typically, humans tend to learn much better when the data examples are organized in a meaningful order rather than randomly presented, e.g., learning from basic easy concepts to advanced hard concepts resembling the “curriculum” taught in schools [Elman, 1993; Rohde and Plaut, 1999] . To this end, curriculum learning (CL) [Bengio et al., 2009; Wang et al., 2021a; Soviany et al., 2022] is proposed to mimic human’s learning process, and has been proved to be effective in boosting the model performances as well as improving the generalization capacity and convergence of learning models in various scenarios including computer vision [Guo et al., 2018; Jiang et al., 2014], natural language processing [Platanios et al., 2019; Tay et al., 2019] etc.  \nCurriculum graph mac","cbCaivKWBWaNxFQf","https://ap.wps.com/l/cbCaivKWBWaNxFQf","pdf",271130,1,10,"English","en",105,"# Introduction\n## Challenges and Problem Formulation\n## Survey Scope and Method Taxonomy\n## Future Research Directions","[{\"question\":\"Why does random training order hurt graph machine learning models?\",\"answer\":\"Randomly sampling graph data during training ignores differences in sample importance and how training order affects optimization, which can lead to suboptimal performance.\"},{\"question\":\"What is curriculum graph machine learning (Graph CL)?\",\"answer\":\"Graph CL integrates graph machine learning with curriculum learning to improve training effectiveness by organizing learning in a meaningful way rather than random order.\"},{\"question\":\"How are existing Graph CL methods organized in the survey?\",\"answer\":\"Methods are categorized into three classes according to graph learning task granularity: node-level, link-level, and graph-level tasks.\"}]","Curriculum Graph Machine Learning - 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