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Graph structured data is ubiquitous in daily life and scientific areas andhas attracted increasing attention. Graph Neural Networks (GNNs) have beenproved to be effective in modeling graph structured data and many variants ofGNN architectures have been proposed. However, much human effort is oftenneeded to tune the architecture depending on different datasets. Researchersnaturally adopt Automated Machine Learning on Graph Learning, aiming to reducethe human effort and achieve generally top-performing GNNs, but their methodsfocus 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. Wereceived top solutions especially from industrial tech companies like Meituan,Alibaba and Twitter, which are already open sourced on Github. After detailedcomparisons with solutions from academia, we quantify the gaps between academiaand industry on modeling scope, effectiveness and efficiency, and show that (1)academia AutoML for Graph solutions focus on GNN architecture search whileindustrial solutions, especially the winning ones in the KDD Cup, tend toobtain an overall solution (2) by neural architecture search only, academiasolutions achieve on average 97.3% accuracy of industrial solutions (3)academia solutions are cheap to obtain with several GPU hours while industrialsolutions take a few months' labors. Academic solutions also contain much fewerparameters.

Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020