  Graph structured data is ubiquitous in daily life and scientific areas and
has attracted increasing attention. Graph Neural Networks (GNNs) have been
proved to be effective in modeling graph structured data and many variants of
GNN architectures have been proposed. However, much human effort is often
needed to tune the architecture depending on different datasets. Researchers
naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce
the human effort and achieve generally top-performing GNNs, but their methods
focus more on the architecture search. To understand GNN practitioners'
automated solutions, we organized AutoGraph Challenge at KDD Cup 2020,
emphasizing on automated graph neural networks for node classification. We
received top solutions especially from industrial tech companies like Meituan,
Alibaba and Twitter, which are already open sourced on Github. After detailed
comparisons with solutions from academia, we quantify the gaps between academia
and industry on modeling scope, effectiveness and efficiency, and show that (1)
academia AutoML for Graph solutions focus on GNN architecture search while
industrial solutions, especially the winning ones in the KDD Cup, tend to
obtain an overall solution (2) by neural architecture search only, academia
solutions achieve on average 97.3% accuracy of industrial solutions (3)
academia solutions are cheap to obtain with several GPU hours while industrial
solutions take a few months' labors. Academic solutions also contain much fewer
parameters.
