Machine learning on graphs 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 solve this critical challenge, automated machine
learning (AutoML) on graphs which combines the strength of graph machine
learning and AutoML together, is gaining attention from the research community.
Therefore, we comprehensively survey AutoML on graphs in this paper, primarily
focusing on hyper-parameter optimization (HPO) and neural architecture search
(NAS) for graph machine learning. We further overview libraries related to
automated graph machine learning and in-depth discuss AutoGL, the first
dedicated open-source library for AutoML on graphs. In the end, we share our
insights on future research directions for automated graph machine learning.
This paper is the first systematic and comprehensive review of automated
machine learning on graphs to the best of our knowledge.