  Recent years have witnessed an upsurge of research interests and applications
of machine learning on graphs. Automated machine learning (AutoML) on graphs is
on the horizon to automatically design the optimal machine learning algorithm
for a given graph task. However, all current libraries cannot support AutoML on
graphs. To tackle this problem, we present Automated Graph Learning (AutoGL),
the first library for automated machine learning on graphs. AutoGL is
open-source, easy to use, and flexible to be extended. Specifically, We propose
an automated machine learning pipeline for graph data containing four modules:
auto feature engineering, model training, hyper-parameter optimization, and
auto ensemble. For each module, we provide numerous state-of-the-art methods
and flexible base classes and APIs, which allow easy customization. We further
provide experimental results to showcase the usage of our AutoGL library.
