  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.
