Introduction

Welcome to the world of Cosmograph! This documentation will guide you through getting started with our tools for building high-performance graph visualizations.

🪐 Cosmograph 2.0

Cosmograph 2.0 is a major update to our Cosmograph toolkit, making it faster, more powerful, and more flexible. It addresses many issues with the original Cosmograph, such as strict data file size limitations, slow filtering, and limited analytical capabilities.

Built from the ground up, Cosmograph 2.0 uses:

• DuckDB (the best in-memory analytics database);
• Mosaic (the fastest cross-filtering and visual analytics framework for the web);
• SQLRooms (an open-source React toolkit for human and agent collaborative analytics apps);
• The latest version of cosmos.gl (our core force simulation and rendering engine, which recently joined OpenJS) to deliver even faster performance, more forces, and the long-awaited point-dragging functionality!

Learn more in the Concept & stack section.

What does this mean in practice?

• Work with larger datasets and use SQL (thanks to WebAssembly and DuckDB);
• Enjoy much better performance (for filtering, timelines, and changing visual properties of the graph, etc.);
• Open Parquet files natively;
• Experience new clustering force;
• Save your graphs to the cloud and share them with the world easily.

Want to learn more?

🧑‍💻 Check out our new app!

⚒️ If you’re building your own tool with Cosmograph, read our new library docs.

🎻 Looking for Classic Cosmograph? It lives at classic.cosmograph.app now.

💻 Web application

Explore the power of Cosmograph web application that enables you to analyze massive graph datasets and machine learning embeddings. Your data never leaves your machine (unless you decide to share it), as all calculations are performed directly on your local GPU.

🐍 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.

📚 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.

💫 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 offers a variety of additional features and components, allowing you to build powerful, interactive large-graph analytical applications quickly and effortlessly.