Graph 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. Specifically, 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, link-level, 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.

翻译：图机器学习已在学术界和工业界得到广泛研究。然而，现有的大多数图机器学习模型在训练时通常以随机顺序处理数据样本，这可能因忽略不同图数据样本及其训练顺序对模型优化状态的重要性而导致性能欠佳。为解决这一关键问题，结合了图机器学习与课程学习优势的课程化图机器学习（Graph CL）应运而生，并吸引了研究界越来越多的关注。因此，本文全面概述了Graph CL的相关方法，并对该方向的最新进展进行了详细综述。具体而言，我们首先讨论了Graph CL面临的关键挑战，并给出了其正式的问题定义。随后，基于三类图机器学习任务（即节点级、链接级和图级任务），我们将现有方法归纳为三类进行总结。最后，我们就未来研究方向提出了见解。据我们所知，本文是首篇关于课程化图机器学习的综述论文。