  Recent years have witnessed an upsurge in research interests and applications
of machine learning on graphs. However, manually designing the optimal machine
learning algorithms for different graph datasets and tasks is inflexible,
labor-intensive, and requires expert knowledge, limiting its adaptivity and
applicability. Automated machine learning (AutoML) on graphs, aiming to
automatically design the optimal machine learning algorithm for a given graph
dataset and task, has received considerable attention. However, none of the
existing libraries can fully support AutoML on graphs. To fill this gap, we
present Automated Graph Learning (AutoGL), the first dedicated library for
automated machine learning on graphs. AutoGL is open-source, easy to use, and
flexible to be extended. Specifically, we propose a three-layer architecture,
consisting of backends to interface with devices, a complete automated graph
learning pipeline, and supported graph applications. The automated machine
learning pipeline further contains five functional modules: auto feature
engineering, neural architecture search, hyper-parameter optimization, model
training, and auto ensemble, covering the majority of existing AutoML methods
on graphs. For each module, we provide numerous state-of-the-art methods and
flexible base classes and APIs, which allow easy usage and customization. We
further provide experimental results to showcase the usage of our AutoGL
library. We also present AutoGL-light, a lightweight version of AutoGL to
facilitate customizing pipelines and enriching applications, as well as
benchmarks for graph neural architecture search. The codes of AutoGL are
publicly available at https://github.com/THUMNLab/AutoGL.
