GRAPE for fast and scalable graph processing and random-walk-based embedding.

ABSTRACT: 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 perform

SUBMITTER: Cappelletti L 

PROVIDER: S-EPMC10768636 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature