re-mocd 0.2.3

Rapid multi-objective community detection with parallel computation and caching to efficiently handle large-scale graphs. With networkx compatibilityy

rapid evolutionary multi-objective community detection algorithms

---

Overview

re-mocd is a Rust-based library designed for efficient and high-performance community detection in graphs. By leveraging the speed and memory safety of Rust, the project aims to handle large-scale graphs while addressing limitations in traditional algorithms, such as Louvain.

---

Installation

Via PyPI

Install the library using pip:

pip install re-mocd

---

Usage

Using re-mocd with a networkx.Graph() is simple. For example:

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)
])

# Pareto envelope-based selection algorithm II (PESA-II) 
partition = re_mocd.pesa_ii_maxq(G, debug=True)
partition = re_mocd.pesa_ii_minimax(G, debug=True)

# Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II)
partition = re_mocd.nsga_ii(G, debug=True)

# You can check the fitness value of the partition
# returned by the algorithm using:
mod = re_mocd.fitness(G, partition)

---

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