Automated Machine Learning on Graphs: A Survey
It provides a systematic review for researchers and practitioners in graph machine learning, but it is incremental as it synthesizes existing work rather than presenting new methods.
This paper surveys automated machine learning (AutoML) on graphs to address the challenge of manually designing optimal algorithms for graph-related tasks, focusing on hyper-parameter optimization and neural architecture search, and introduces AutoGL as the first dedicated open-source library for this area.
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.