OGB-dataset is a collection of benchmark datasets for graph machine learning, published on Kaggle. The specific datasets, scale, and creation details are not provided in the available metadata. Further details about the data content, such as the number of graphs, nodes, or edges, require verification after download.
Use Cases
- Benchmarking graph neural network performance on node classification tasks (inferred from domain, verify after download)
- Evaluating link prediction algorithms on large-scale networks (inferred from domain, verify after download)
- Training models for graph-level property prediction (inferred from domain, verify after download)
Strengths
- Published on the Kaggle platform, which provides a standardized interface for access and versioning.
Limitations
- Metadata is minimal; actual content requires verification after download.
- Row count, file formats, and column definitions are unknown, which limits suitability assessment.
- License, author, and last update information are unavailable.