Open Graph Benchmark

Benchmark datasets, data loaders and evaluators for graph machine learning

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The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs.

OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader, which is fully compatible with Pytorch Geometric and DGL. The model performance can be evaluated using the OGB Evaluator in a unified manner.

OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. Subscribe to our google group to ask us questions and keep up to date with major changes to the datasets.

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Realistic datasets

OGB provides a diverse set of challenging and realistic benchmark datasets that are of varying sizes and cover a variety graph machine learning tasks, including prediction of node, link, and graph properties.

Flexible data loaders

OGB fully automates dataset processing. The OGB data loaders automatically download and process graphs, provide graph objects that are fully compatible with Pytorch Geometric and DGL.

Unified evaluation

OGB provides standardized dataset splits and evaluators that allow for easy and reliable comparison of different models in a unified manner. OGB uses leaderboards to keep track of the state-of-the-art.