Add graph-algorithms: Fast approximate minimum cycle basis (MCB) for sparse graphs

[m15154178071-cmyk]

A high-performance pure-Python implementation for minimum cycle basis and induced cycle detection.

Project

graph-algorithms

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Why This Project Is Awesome

Which criterion does it meet? (pick one)

• "Industry Standard"
• "Rising Star"
• "Hidden Gem"

Explain:

This library implements a fast heuristic for Minimum Cycle Basis (MCB) that is up to 355x faster than NetworkX on sparse/planar graphs (e.g., grids, molecular graphs), with <3% error on general random graphs. It's written in pure Python with zero dependencies and includes a NetworkX-compatible adapter.

How It Differs

If similar entries exist, what makes this one unique?

Unlike educational algorithm repos like TheAlgorithms, this is a production-ready tool optimized for real-world performance in computational chemistry, EDA, and network analysis. It achieves C-extension-level speed in pure Python.

[JinyangWang27]

@m15154178071-cmyk Thank you for your submission. Unfortunately, this PR doesn't meet our acceptance criteria:

Repository age: The repository was created 3 weeks ago. We require a minimum of 3 months (or 6 months for Hidden Gem submissions) to ensure project stability.
GitHub stars: 0 stars (minimum 100 required, or strong justification for Hidden Gem).

Please see our CONTRIBUTING.md for full requirements. You're welcome to resubmit once the project has matured and gained community traction.