We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale  , encompass multiple important graph ML tasks  and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs,  and knowledge graphs. For each dataset, we provide a unified evaluation protocol using  application-specific data splits and evaluation metrics. Our empirical investigation reveals the challenges of existing graph methods in handling large-scale graphs and predicting out-of-distribution data. OGB presents an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders and evaluation scriptsare available at URL .
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale (up to 100+ million nodes and 1+ billion edges) , encompass multiple important graph ML tasks , and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders , evaluation scripts, baseline code, and leaderboards are publicly available at URL .
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(up to 100+ million nodes and 1+ billion edges)
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,
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source code ASTs,
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meaningful
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Our empirical investigation reveals the challenges of existing graph methods in handling
In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to
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predicting
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data. OGB presents
generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides
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and evaluation scriptsare
, evaluation scripts, baseline code, and leaderboards are publicly