GRAPE is a software resource for graph processing, learning and embedding that is orders of magnitude faster than existing state-of-the-art libraries. GRAPE can quickly process real-world graphs with millions of nodes and billions of edges, enabling complex graph analyses and research in graph-based machine learning and in diverse disciplines.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Li, M. M., Huang, K. & Zitnik, M. Graph representation learning in biomedicine and healthcare. Nat. Biomed. Eng. 6, 1353–1369 (2022). A review article that presents applications of GRL in medicine.
Xia, F. et al. Graph learning: a survey. IEEE Trans. Artif. Intell. 2, 109–127 (2021). A review article that presents graph learning algorithms.
Zhang, D., Yin, J., Zhu, X. & Zhang, C. Network representation learning: a survey. IEEE Trans. Big Data 1, 3–28 (2020). A review article that considers scalability issues of network representation learning algorithms.
Perkel, J. M. Why scientists are turning to Rust. Nature 588, 185–186 (2020). An opinion piece explaining the reasons that the Rust language has been successful for scientific computation.
Elias, P. Efficient storage and retrieval by content and address of static files. J. ACM 21, 246–260 (1974). A paper that introduces the theoretical basis on which quasi-succinct data structures are grounded.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Cappelletti, L. et al. GRAPE for fast and scalable graph processing and random-walk-based embedding. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00465-8 (2023).
Rights and permissions
About this article
Cite this article
A software resource for large graph processing and analysis. Nat Comput Sci 3, 586–587 (2023). https://doi.org/10.1038/s43588-023-00466-7
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00466-7