Introduction
Welcome to the world of Cosmograph! This documentation will guide you through getting started with our tools for building high-performance graph visualizations.
If you’re planning to use Cosmograph in your work, follow the Citing and licensing section.
💫 Cosmograph vs. Cosmos.gl
Cosmograph is built on top of cosmos.gl, a high-performance WebGL graph rendering engine. While cosmos.gl provides the core GPU-accelerated graphics, Cosmograph simplifies its use by offering a range of components and features that make it easier to build powerful, interactive graph visualizations.
Cosmos.gl website
💻 Web application
Explore the power of Cosmograph web application that enables you to analyze massive graph datasets and machine learning embeddings. Your data stays secure as all calculations are performed directly on your GPU.
Guide to the web application
🐍 Python Widget
Bring the power of Cosmograph directly to your Python notebooks with our interactive widget. Perfect for data scientists and researchers who want to visualize network graphs and embeddings right in their Jupyter environment. The widget provides seamless integration with popular Python data science libraries and maintains the same high-performance GPU-accelerated rendering.
Python widget guide
📚 JavaScript / React library
The fastest web-based library for large network graph visualization built on top of WebGL. You can use it to add blazingly fast network graph and embeddings visualizations to your own web application, and amplify them with ready-to-use interactive components.
Library guide
Graph visualizations are fascinating, especially when you’re dealing with large graphs. We hope you’ll find Cosmograph useful and that it will help you with your projects. We can’t wait to see what you build with it! If you like Cosmograph, please share it with your friends and colleagues, and give us your feedback!