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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.
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.
Open Graph Benchmark (OGB) provides large-scale graph datasets and evaluation frameworks for node property prediction tasks. Learn about the datasets, tasks, metrics, and leaderboards for different domains such as products, proteins, arxiv, papers, and more.
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.
6 de abr. de 2023 · 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.
The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover a variety of graph machine learning tasks and real-world applications.
