Scientific data articles from across Nature Portfolio

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.

As artificial intelligence (AI) makes increasingly impressive contributions to science, scientists increasingly want to understand how AI reaches its conclusions. Matthew D. Schwartz discusses what it means to understand AI and whether such a goal is achievable — or even needed.

Designing efficient bike path networks requires balancing multiple opposing constraints such as cost and safety. An adaptive demand-driven inverse percolation approach is proposed to generate efficient network structures by explicitly taking into account the demands of cyclists and their route choice behavior based on safety preferences.

The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and existing solutions still fall short of their promises.

Networks offer a powerful visual representation of complex systems. Cartographs introduce a diverse set of network layouts for highlighting and visually inspecting chosen characteristics of a network. The resulting visualizations are interpretable and can be used to explore complex datasets, such as large-scale biological networks.