OGB-LSC (OGB Large-Scale Challenge)

Introduced by Hu et al. in OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

OGB Large-Scale Challenge (OGB-LSC) is 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.

OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.

  • MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification).
  • WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction).
  • PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).


Paper Code Results Date Stars

Dataset Loaders

No data loaders found. You can submit your data loader here.


Similar Datasets


  • Unknown