# Rapid Multi-Objective Community Detection
> **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.
> **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: `{}`.
### Installation via PyPI
The library is available on PyPI. You can install it using `pip`:
```bash
pip install rmocd
```
#### Simple Example (Python)
Here's a simple example of how to use the library in Python:
```python
import rmocd
edgelist_file = "res/graphs/artificials/article.edgelist"
partition, modularity = rmocd.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/rmocd
cd rmocd
```
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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