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A software resource for large graph processing and analysis

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.

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Fig. 1: High-level structure and functionalities of GRAPE.

References

  1. 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.

    Article  Google Scholar 

  2. Xia, F. et al. Graph learning: a survey. IEEE Trans. Artif. Intell. 2, 109–127 (2021). A review article that presents graph learning algorithms.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  MathSciNet  MATH  Google Scholar 

Download references

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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).

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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

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