Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020. (arXiv:2204.02625v1 [cs.LG])

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 …

academia arxiv challenge gap industry