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Can I read GRAPE for fast and scalable graph processing and random-walk-based embedding on EtoBox?
GRAPE for fast and scalable graph processing and random-walk-based embedding by Luca Cappelletti; Tommaso Fontana; Elena Casiraghi; Vida Ravanmehr; Tiffany J. Callahan; Carlos Cano; Marcin P. Joachimiak; Christopher J. Mungall; Peter N. Robinson; Justin Reese; Giorgio Valentini is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is GRAPE for fast and scalable graph processing and random-walk-based embedding about?
Graph representation learning methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE (Graph Representation Learning, Prediction and Evaluation), a software resource for graph processing and embedding that is able to scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random-walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as competitive edge- and node-label prediction performance. GRAPE comprises approximately 1.7 million well-documented lines of Python and Rust code and provides 69 node-embedding methods, 25 inference models, a collection of efficient graph-processing utilities, and over 80,000 graphs from the literature and other sources. Standardized interfaces allow a seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an
Who reads GRAPE for fast and scalable graph processing and random-walk-based embedding?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Luca Cappelletti; Tommaso Fontana; Elena Casiraghi; Vida Ravanmehr; Tiffany J. Callahan; Carlos Cano; Marcin P. Joachimiak; Christopher J. Mungall; Peter N. Robinson; Justin Reese; Giorgio Valentini
- Publisher
- Nature Publishing Group US
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)