<div align="center">
<img src="res/logo.png" alt="logo" style="width: 40%;">
<strong>Rapid Enhanced Multi-objective Community Detection Algorithm</strong>
[](https://pypistats.org/packages/re_mocd)
</div>
> [!IMPORTANT]
> **re-mocd** is an open source Rust-based library designed to provide a simple and easy-to-use multi-objective algorithm for efficient and high-performance community detection on graphs. You can use it to make tests on your own graphs, or to make comparisons, be free ☺
---
## Installation
### Via PyPI
Install the library using pip:
```bash
pip install re-mocd
```
---
## Usage
### From `networkx.Graph()`
Using **re-mocd** with a `networkx.Graph()` is simple. For example:
```python
import networkx as nx
import re_mocd
# Create a graph
G = nx.Graph([
(0, 1), (0, 3), (0, 7),
(1, 2), (1, 3), (1, 5),
(2, 3),
(3, 6),
(4, 5), (4, 6),
(5, 6),
(7, 8)
])
# Random networks help validate the detection of overlapping communities
# by serving as a baseline for comparison. These structures appear as significant
# deviations from the expected behavior in unorganized networks, allowing the method
# to highlight more complex patterns, such as overlapping communities.
# However, generating random networks and their Pareto fronts increases the runtime.
# Higher values are recommended for large numbers of overlapping communities
random_networks = 3
# The main function that will perform the search for communities in the graph.
# If you want a fast search, keep the number of random networks low.
partition = re_mocd.rmocd(G, random_networks)
# You can see its fitness function using the function below.
# (check section "Fitness Function" to see how it is calculated).
mod = re_mocd.modularity(G, partition)
```
### Examples
- [Plotting Example](tests/python/example.py)
- [Comparison with Other Algorithms](tests/python/main.py)
- [Modularity ring problem](tests/python/benchmarks/ring.py)
- [Single file test](tests/python/benchmarks/single.py)
---
<center>
<img src="res/example.png" alt="Example Plot" width="600">
</center>
---
## Running from Scratch
### Build and Run
1. Clone the repository:
```bash
git clone https://github.com/0l1ve1r4/re_mocd
cd re_mocd
```
2. Rename main (it is like this to avoid unused warnings):
```bash
mv cli.rs main.rs
```
3. Compile and execute the algorithm:
```bash
cargo run --release mygraph.edgelist
```
### Debug Mode
Use the `-d` flag for additional debug output:
```bash
cargo run --release mygraph.edgelist -d
```
---
### Fitness Function
1. **Intra Objective:** Maximize the density of connections within communities:
$$\text{intra}(C) = 1 - \frac{\sum_{c \in C} |E(c)|}{m}$$
2. **Inter Objective:** Minimize the strength of connections between communities:
$$\text{inter}(C) = \sum_{c \in C} \left( \frac{\sum_{v \in c} \text{deg}(v)}{2m} \right)^2$$
3. **Modularity Function:** Combines both:
$$Q(C) = 1 - \text{intra}(C) - \text{inter}(C)$$
These two conflicting objectives balance the density of internal connections and the sparsity of external connections. They are optimized simultaneously.
### Contributing
Contributions are welcome! Feel free to submit issues, feature requests, or pull requests to improve the project.
**License:** GPL-3.0 or later
**Author:** [Guilherme Santos](https://github.com/0l1ve1r4)
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"description": "<div align=\"center\">\n <img src=\"res/logo.png\" alt=\"logo\" style=\"width: 40%;\"> \n\n <strong>Rapid Enhanced Multi-objective Community Detection Algorithm</strong>\n\n\n\n\n[](https://pypistats.org/packages/re_mocd)\n\n</div>\n\n> [!IMPORTANT] \n> **re-mocd** is an open source Rust-based library designed to provide a simple and easy-to-use multi-objective algorithm for efficient and high-performance community detection on graphs. You can use it to make tests on your own graphs, or to make comparisons, be free \u263a\n\n\n---\n\n## Installation \n\n### Via PyPI \n\nInstall the library using pip: \n```bash\npip install re-mocd\n```\n\n---\n\n## Usage \n\n### From `networkx.Graph()` \n\nUsing **re-mocd** with a `networkx.Graph()` is simple. For example: \n```python\nimport networkx as nx \nimport re_mocd\n\n# Create a graph\nG = nx.Graph([\n (0, 1), (0, 3), (0, 7), \n (1, 2), (1, 3), (1, 5), \n (2, 3), \n (3, 6), \n (4, 5), (4, 6), \n (5, 6), \n (7, 8)\n])\n\n# Random networks help validate the detection of overlapping communities \n# by serving as a baseline for comparison. These structures appear as significant \n# deviations from the expected behavior in unorganized networks, allowing the method\n# to highlight more complex patterns, such as overlapping communities. \n# However, generating random networks and their Pareto fronts increases the runtime. \n# Higher values \u200b\u200bare recommended for large numbers of overlapping communities\nrandom_networks = 3\n\n# The main function that will perform the search for communities in the graph. \n# If you want a fast search, keep the number of random networks low.\npartition = re_mocd.rmocd(G, random_networks)\n\n# You can see its fitness function using the function below. \n# (check section \"Fitness Function\" to see how it is calculated).\nmod = re_mocd.modularity(G, partition)\n```\n\n### Examples \n\n- [Plotting Example](tests/python/example.py) \n- [Comparison with Other Algorithms](tests/python/main.py) \n- [Modularity ring problem](tests/python/benchmarks/ring.py)\n- [Single file test](tests/python/benchmarks/single.py)\n\n---\n\n<center> \n<img src=\"res/example.png\" alt=\"Example Plot\" width=\"600\"> \n</center> \n\n---\n\n## Running from Scratch \n\n### Build and Run \n\n1. Clone the repository: \n ```bash\n git clone https://github.com/0l1ve1r4/re_mocd\n cd re_mocd\n ```\n\n2. Rename main (it is like this to avoid unused warnings):\n ```bash\n mv cli.rs main.rs\n ```\n\n3. Compile and execute the algorithm: \n ```bash\n cargo run --release mygraph.edgelist\n ```\n\n### Debug Mode \n\nUse the `-d` flag for additional debug output: \n```bash\ncargo run --release mygraph.edgelist -d\n```\n\n---\n\n### Fitness Function\n\n1. **Intra Objective:** Maximize the density of connections within communities:\n\n $$\\text{intra}(C) = 1 - \\frac{\\sum_{c \\in C} |E(c)|}{m}$$\n\n2. **Inter Objective:** Minimize the strength of connections between communities:\n\n $$\\text{inter}(C) = \\sum_{c \\in C} \\left( \\frac{\\sum_{v \\in c} \\text{deg}(v)}{2m} \\right)^2$$\n\n3. **Modularity Function:** Combines both:\n\n $$Q(C) = 1 - \\text{intra}(C) - \\text{inter}(C)$$\n\n\nThese two conflicting objectives balance the density of internal connections and the sparsity of external connections. They are optimized simultaneously.\n\n\n### Contributing \n\nContributions are welcome! Feel free to submit issues, feature requests, or pull requests to improve the project. \n\n**License:** GPL-3.0 or later \n**Author:** [Guilherme Santos](https://github.com/0l1ve1r4) \n",
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