<div align="center">
<!-- <img src="" alt="logo" style="width: 50%;"> -->
<p align="center">
<strong>Rapid Multi-Objective Community Detection</strong>
</p>
<hr>
[](https://pypistats.org/packages/re_mocd)
</div>
> **Warning:**
>
> This project is in its early stages, and the algorithm is still being refined. Performance and results may not be optimal yet.
## Overview
This project aims to develop a high-performance genetic algorithm in Rust to detect communities in a graph. The goal is to optimize the community detection process to handle large-scale graphs efficiently. This algorithm also tries to handle some problems that happens with louvain algorithm.
> **Why Rust?**
>
> Rust is one of the fastest programming languages available, offering high-performance execution and memory safety without a garbage collector. It has a rich ecosystem of libraries.
## Usage
### Requirements
Before running the algorithm, you'll need an edge list file formatted as follows:
```plaintext
0,1,{'weight': 4}
0,2,{'weight': 5}
0,3,{'weight': 3}
...
0,10,{'weight': 2}
```
The **weight** attribute is optional. If not provided, it can be represented by an empty dictionary: `{}`. You can save a `networkx` graph like this with: `networkx.write_edgelist(G, file_path, delimiter=",", data=False)`
### Installation via PyPI
The library is available on PyPI. You can install it using `pip`:
```bash
pip install re_mocd
```
#### Simple Example (Python)
Here's a simple example of how to use the library in Python, the function return a dict of the partition found:
```python
import re_mocd
edgelist_file = "res/graphs/artificials/article.edgelist"
partition = re_mocd.run(edgelist_file)
```
You can see an [example of plotting](res/example.py), or an example of how to [make comparisons](res/example.py) with other algorithms in `res/`.
<center>
</center>
### Running from Scratch
#### Build and Run
1. Clone the repository and navigate to the project folder:
```bash
git clone https://github.com/0l1ve1r4/re_mocd
cd re_mocd
```
2. Compile and run the algorithm with your edge list file:
```bash
cargo run --release mygraph.edgelist
```
#### Debug Mode
To run the algorithm in debug mode, use the `-d` flag:
```bash
cargo run --release mygraph.edgelist -d
```
This will provide additional debug output, useful for troubleshooting and monitoring the algorithm's progress.
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"description": "<div align=\"center\">\n <!-- <img src=\"\" alt=\"logo\" style=\"width: 50%;\"> -->\n\n<p align=\"center\">\n <strong>Rapid Multi-Objective Community Detection</strong>\n </p>\n\n<hr>\n\n\n\n\n[](https://pypistats.org/packages/re_mocd)\n\n</div>\n\n\n> **Warning:** \n>\n> This project is in its early stages, and the algorithm is still being refined. Performance and results may not be optimal yet.\n\n## Overview\n\nThis project aims to develop a high-performance genetic algorithm in Rust to detect communities in a graph. The goal is to optimize the community detection process to handle large-scale graphs efficiently. This algorithm also tries to handle some problems that happens with louvain algorithm.\n\n> **Why Rust?** \n>\n> Rust is one of the fastest programming languages available, offering high-performance execution and memory safety without a garbage collector. It has a rich ecosystem of libraries.\n\n## Usage\n\n### Requirements\n\nBefore running the algorithm, you'll need an edge list file formatted as follows:\n\n```plaintext\n0,1,{'weight': 4}\n0,2,{'weight': 5}\n0,3,{'weight': 3}\n...\n0,10,{'weight': 2}\n```\n\nThe **weight** attribute is optional. If not provided, it can be represented by an empty dictionary: `{}`. You can save a `networkx` graph like this with: `networkx.write_edgelist(G, file_path, delimiter=\",\", data=False)`\n\n### Installation via PyPI\n\nThe library is available on PyPI. You can install it using `pip`:\n\n```bash\npip install re_mocd\n```\n\n#### Simple Example (Python)\n\nHere's a simple example of how to use the library in Python, the function return a dict of the partition found:\n\n```python\nimport re_mocd\n\nedgelist_file = \"res/graphs/artificials/article.edgelist\"\npartition = re_mocd.run(edgelist_file)\n```\n\nYou can see an [example of plotting](res/example.py), or an example of how to [make comparisons](res/example.py) with other algorithms in `res/`.\n\n<center>\n\n\n\n</center>\n\n### Running from Scratch\n\n#### Build and Run\n\n1. Clone the repository and navigate to the project folder:\n ```bash\n git clone https://github.com/0l1ve1r4/re_mocd\n cd re_mocd\n ```\n\n2. Compile and run the algorithm with your edge list file:\n ```bash\n cargo run --release mygraph.edgelist\n ```\n\n#### Debug Mode\n\nTo run the algorithm in debug mode, use the `-d` flag:\n```bash\ncargo run --release mygraph.edgelist -d\n```\n\nThis will provide additional debug output, useful for troubleshooting and monitoring the algorithm's progress.\n",
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