  Enabling effective and efficient machine learning (ML) over large-scale graph
data (e.g., graphs with billions of edges) can have a great impact on both
industrial and scientific applications. However, existing efforts to advance
large-scale graph ML have been largely limited by the lack of a suitable public
benchmark. Here we present OGB Large-Scale Challenge (OGB-LSC), a collection of
three real-world datasets for facilitating the advancements in large-scale
graph ML. The OGB-LSC datasets are orders of magnitude larger than existing
ones, covering three core graph learning tasks -- link prediction, graph
regression, and node classification. Furthermore, we provide dedicated baseline
experiments, scaling up expressive graph ML models to the massive datasets. We
show that expressive models significantly outperform simple scalable baselines,
indicating an opportunity for dedicated efforts to further improve graph ML at
scale. Moreover, OGB-LSC datasets were deployed at ACM KDD Cup 2021 and
attracted more than 500 team registrations globally, during which significant
performance improvements were made by a variety of innovative techniques. We
summarize the common techniques used by the winning solutions and highlight the
current best practices in large-scale graph ML. Finally, we describe how we
have updated the datasets after the KDD Cup to further facilitate research
advances. The OGB-LSC datasets, baseline code, and all the information about
the KDD Cup are available at https://ogb.stanford.edu/docs/lsc/ .
