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Learn about the datasets

  • Please learn about the datasets and their usage.

Installing Python Package

First, please install our ogb Python package, as all of our datasets are downloaded and prepared using the package. The model evaluation and test submission file preparation are also handled by our package. Please install/update it by:

pip install -U ogb

Summary of Datasets

OGB-LSC provides three large-scale datasets. The dataset statistics as well as basic information are summarized below. Each dataset is described in detail in the dataset page (jump to the links).

Task category Name Package #Graphs #Total nodes #Total edges Task Type Metric Download size
Node-level MAG240M >=1.3.2 1 244,160,499 1,728,364,232 Multi-class classification Accuracy 167GB
Link-level WikiKG90Mv2 >=1.3.3 1 91,230,610 601,062,811 KG completion MRR 89GB
Graph-level PCQM4Mv2 >=1.3.2 3,746,619 52,970,652 54,546,813 Regression MAE 59MB

: The PCQM4Mv2 dataset is provided in the SMILES strings. After processing them into graph objects, the eventual file size will be around 8GB.

Important: Make sure below prints the required package version for the dataset you are working on.

python -c "import ogb; print(ogb.__version__)"


In our paper, we further perform an extensive baseline analysis on each dataset, implementing simple baseline models as well as advanced expressive models at scale. We find that advanced expressive models, despite requiring more efforts to scale up, do benefit from large data and significantly outperform simple baseline models that are easy to scale. All of our baseline code is made publicly available to facilitate public research. Please also check out public leaderboards (evaluated on test-dev set) for state-of-the-art submissions.