[PR #2898] [CLOSED] Add graph-algorithms: Fast approximate minimum cycle basis (MCB) for sparse graphs #6611

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GiteaMirror · 2026-04-17T07:34:19-05:00

GiteaMirror commented

2026-04-17 07:34:19 -05:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/2898
Author: @m15154178071-cmyk
Created: 2/2/2026
Status: ❌ Closed

Base: master ← Head: patch-1

📝 Commits (1)

0d815fe Add graph-algorithms to Algorithms section

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

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

Project

graph-algorithms

Checklist

One project per PR
PR title format: 'Add project-name'
Entry format: 'project-name - Description ending with period.'
Description is concise and short

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.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/2898 **Author:** [@m15154178071-cmyk](https://github.com/m15154178071-cmyk) **Created:** 2/2/2026 **Status:** ❌ Closed **Base:** `master` ← **Head:** `patch-1` --- ### 📝 Commits (1) - [`0d815fe`](https://github.com/vinta/awesome-python/commit/0d815feb7a01832a7c0cdf91fa6840dcec141e80) Add graph-algorithms to Algorithms section ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description A high-performance pure-Python implementation for minimum cycle basis and induced cycle detection. ## Project [graph-algorithms](https://github.com/m15154178071-cmyk/graph-algorithms) ## Checklist - [x] One project per PR - [x] PR title format: 'Add project-name' - [x] Entry format: '[project-name](url) - Description ending with period.' - [x] Description is concise and short ## Why This Project Is Awesome Which criterion does it meet? (pick one) - [ ] "Industry Standard" - [ ] "Rising Star" - [x] "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. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-17 07:34:20 -05:00

GiteaMirror closed this issue

2026-04-17 07:34:21 -05:00

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Reference: github-starred/awesome-python#6611