| Popis: |
Graph Representation Learning methods opened new possibilities for addressing complex,real-world problems represented by graphs. However, many graphs used in these applicationscomprise millions of nodes and billions of edges and are beyond the capabilities of current methodsand software implementations. We present GRAPE, a software resource for graph processing andrepresentation learning that is able to scale with big graphs by using specialized and smart datastructures, algorithms, and a fast parallel implementation. When compared with state of the artsoftware resources, GRAPE shows an improvement of orders of magnitude in empirical space andtime complexity, as well as a substantial and statistically significant improvement in edge predictionand node label prediction performance. Furthermore, GRAPE provides over 80,000 graphs fromthe literature and other sources, standardized interfaces allowing a straightforward integration ofthird-party libraries, 61 node embedding methods, 25 inference models, and 3 modular pipelinesto allow a FAIR and reproducible comparison of methods and libraries for graph processing andembedding. |
