Metadata-Version: 2.4
Name: pymocd
Version: 1.1.3
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Rust
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
License-File: LICENSE
Author: Guilherme Santos
Author-email: "Guilherme O. Santos" <guilherme.os1@aluno.ufop.edu.br>, Lucas Salgado Vieira <lucas.sv@aluno.ufop.edu.br>
License: GPL-3.0-or-later
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Project-URL: Documentation, https://oliveira-sh.github.io/pymocd/
Project-URL: Repository, https://github.com/ol1veir4s/pymocd
Project-URL: Issues, https://github.com/ol1veir4s/pymocd/issues

<div align="center">
  <img src="res/logo.png" alt="pymocd logo" width="50%">  
  
  <strong>Python Multi-Objective Community Detection Algorithms</strong>  

[![PyPI Publish](https://github.com/oliveira-sh/pymocd/actions/workflows/release.yml/badge.svg)](https://github.com/oliveira-sh/pymocd/actions/workflows/release.yml)
![Rust Compilation](https://img.shields.io/github/actions/workflow/status/oliveira-sh/pymocd/rust.yml)
![PyPI - Version](https://img.shields.io/pypi/v/pymocd)
![PyPI - License](https://img.shields.io/pypi/l/pymocd)

</div>

**pymocd** is a Python library, powered by a Rust backend, for performing efficient multi-objective evolutionary community detection in complex networks. This library is designed to deliver enhanced performance compared to traditional methods, making it particularly well-suited for analyzing large-scale graphs.

**Navigate the [Documentation](https://www.google.com/search?q=https://oliveira-sh.github.io/pymocd/) for detailed guidance and usage instructions.**

## Table of Contents
- [Understanding Community Detection with HP-MOCD](#understanding-community-detection-with-hp-mocd)
- [Getting Started](#getting-started)
  - [Key Features](#key-features)
- [Contributing](#contributing)
- [Citation](#citation)

---

### Understanding Community Detection with HP-MOCD

The `HP-MOCD` algorithm, central to `pymocd`, identifies community structures within a graph. It proposes a solution by grouping nodes into distinct communities, as illustrated below:

| Original Graph                         | Proposed Community Structure             |
| :------------------------------------: | :--------------------------------------: |
|  ![](res/original_graph.png)           | ![](res/proposed_solution.png)           |

### Getting Started

Installing the library using pip interface:

```bash
pip install pymocd
```

For an easy usage:

```python
import networkx
import pymocd

G = networkx.Graph() # Your graph
alg = pymocd.HpMocd(G)
communities = alg.run()
```
> [!IMPORTANT]
> Graphs must be provided in **NetworkX** or **Igraph** compatible format**.

Refer to the official **[Documentation](https://oliveira-sh.github.io/pymocd/)** for detailed instructions and more usage examples.

### Contributing

We welcome contributions to `pymocd`\! If you have ideas for new features, bug fixes, or other improvements, please feel free to open an issue or submit a pull request. This project is licensed under the **GPL-3.0 or later**.

---

### Citation

If you use `pymocd` or the `HP-MOCD` algorithm in your research, please cite the following paper:

```bibtex
@article{santos2025hpmocd,
  author    = {Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti, Carlos H. G. Ferreira and Gladston J. P. Moreira},
  title     = {HP-MOCD: A High-Performance Multi-Objective Community Detection Algorithm for Large-Scale Networks},
  journal   = {The 17th International Conference on Advances in Social Networks Analysis and Mining},
  year      = {2025},
  abstract  = {Community detection in social networks has traditionally been approached as a single-objective optimization problem, with various heuristics targeting specific community-defining metrics. However, this approach often proves inadequate for capturing the multifaceted nature of communities. We introduce HP-MOCD, a fully parallelized, evolutionary high-performance multi-objective community detection algorithm designed specifically for large-scale networks. Our implementation overcomes the computational challenges that typically limit multi-objective approaches in this domain. While performance may decrease with networks containing high proportions of inter-community edges, extensive evaluations on synthetic datasets demonstrate that HP-MOCD achieves an exceptional balance between scalability and detection accuracy. Available as open-source software, HP-MOCD offers researchers and practitioners a practical, powerful solution for complex network analysis, particularly for applications requiring both efficiency and detection quality.},
  keywords  = {community detection, complex networks, evolutionary algorithms, genetic algorithms, multi-objective},
  note      = {Currently under analysis by the journal}
}
```

