  Enabling effective and efficient machine learning (ML) over large-scale graph
data (e.g., graphs with billions of edges) can have a huge impact on both
industrial and scientific applications. However, community efforts to advance
large-scale graph ML have been severely limited by the lack of a suitable
public benchmark. For KDD Cup 2021, we present OGB Large-Scale Challenge
(OGB-LSC), a collection of three real-world datasets for advancing the
state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that
are orders of magnitude larger than existing ones and covers three core graph
learning tasks -- link prediction, graph regression, and node classification.
Furthermore, OGB-LSC provides dedicated baseline experiments, scaling up
expressive graph ML models to the massive datasets. We show that the expressive
models significantly outperform simple scalable baselines, indicating an
opportunity for dedicated efforts to further improve graph ML at scale. Our
datasets and baseline code are released and maintained as part of our OGB
initiative (Hu et al., 2020). We hope OGB-LSC at KDD Cup 2021 can empower the
community to discover innovative solutions for large-scale graph ML.
