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Graph Visualizer 🗺️

License: MIT PRs Welcome

A lightweight, browser-based graph visualizer designed to help students and developers visualize graphs and practice graph algorithms. Perfect for algorithm studies and note-taking in tools like Obsidian.

✨ Features

  • Simple Input Format: Input graphs using the format A-B 2 (Node1-Node2 Weight)
  • Export Options: Save graph as PNG for your notes or presentations
  • No Dependencies: Pure HTML, CSS, and JavaScript - runs directly in your browser
  • Responsive Design: Works on both desktop and mobile devices

🚀 Motivation

When I was studying algorithms and data structures as a course in university, I often found myself needing to visualize graphs from my studies. When practicing with AI tools like ChatGPT, I'd receive graph definitions in formats like A-B 2 (representing an edge between node A and B with weight 2), but had to draw them manually to visualize them properly.

This tool was born out of that need - a simple, no-frills way to quickly visualize graphs without leaving the browser.

🖥️ Usage

🌐 Recommended (Online)

The easiest way is to use the live version—no setup required:

👉 Open Graph Visualizer


💻 Offline (Local Setup)

If you prefer running it locally:

  1. Clone the repo

    git clone https://github.com/tomcegraphics/graph-visualizer.git
    cd graph-visualizer
    
  2. Open the app

    • Simply double-click index.html
    • Or serve it locally for a smoother dev experience:
    python -m http.server

    Then visit: http://localhost:8000

📸 Examples in Obsidian

Graph Visualization

Graph in Obsidian Graph in Obsidian

With Algorithm Notes

Graph with Notes Graph with Notes Graph with Notes

🛠️ Development

This is a simple, self-contained project with no build step required.

To contribute or modify:

  1. Fork the repository
  2. Make your changes
  3. Open a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Built with Sigma.js for graph visualization
  • Inspired by countless algorithm study sessions
  • Special thanks to the open source community for amazing tools and resources

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