Welcome to cugraph’s documentation!

RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices.

Contents:

• Basics
  • cuGraph Introduction
  • Vision
  • Terminology
  • Roadmap
  • cuGraph BLOGS and Presentations
  • References
  • NetworkX Compatibility and Transition
• API reference
  • Graph Classes
  • Graph Implementation
  • Centrality
  • Community
  • Components
  • Cores
  • Layout
  • Linear Assignment
  • Link Analysis
  • Link Prediction
  • Sampling
  • Traversal
  • Tree
  • Generator
  • DASK MG Helper functions
  • Multi-GPU with cuGraph
  • pylibcugraph

Indices and tables

• Index

• Module Index

• Search Page