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OGB is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. It provides data loaders, evaluators, and leaderboards for graph tasks in PyTorch.
13 de dic. de 2019 · ogb is a collection of graph datasets, data loaders, and evaluators for graph machine learning tasks. It supports PyTorch Geometric and DGL frameworks and provides standardized performance evaluation.
OGB provides graph datasets, data loaders, and evaluators for graph machine learning in PyTorch. Learn how to install, use, and cite OGB for your research.
OGB is a collection of datasets, data loaders, and evaluators for graph machine learning tasks such as node, link, and graph prediction. It supports PyTorch Geometric and DGL frameworks and provides standardized performance evaluation.
Graph: The ogbn-proteins dataset is an undirected, weighted, and typed (according to species) graph. Nodes represent proteins, and edges indicate different types of biologically meaningful associations between proteins, e.g., physical interactions, co-expression or homology [1,2].
OGB is a collection of realistic, large-scale, and diverse datasets for graph-based machine learning tasks. It provides data loaders, evaluators, and papers for 16 benchmarks, such as node property prediction, link prediction, and graph classification.
2 de may. de 2020 · OGB is a collection of diverse and challenging benchmark datasets for graph machine learning research. It provides unified evaluation protocols, data splits, metrics, and a standardized graph ML pipeline for each dataset.
