  Graph machine learning has been extensively studied in both academic and
industry. However, as the literature on graph learning booms with a vast number
of emerging methods and techniques, it becomes increasingly difficult to
manually design the optimal machine learning algorithm for different
graph-related tasks. To tackle the challenge, automated graph machine learning,
which aims at discovering the best hyper-parameter and neural architecture
configuration for different graph tasks/data without manual design, is gaining
an increasing number of attentions from the research community. In this paper,
we extensively discuss automated graph machine learning approaches, covering
hyper-parameter optimization (HPO) and neural architecture search (NAS) for
graph machine learning. We briefly overview existing libraries designed for
either graph machine learning or automated machine learning respectively, and
further in depth introduce AutoGL, our dedicated and the world's first
open-source library for automated graph machine learning. Also, we describe a
tailored benchmark that supports unified, reproducible, and efficient
evaluations. Last but not least, we share our insights on future research
directions for automated graph machine learning. This paper is the first
systematic and comprehensive discussion of approaches, libraries as well as
directions for automated graph machine learning.
