re-mocd 0.1.2

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

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[!NOTE]
This project is in its early stages. Performance and results may not be optimal yet.

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.

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Installation

Via PyPI

Install the library using pip:

pip install re-mocd

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Usage

From networkx.Graph()

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)
])

# Detect communities
partition = re_mocd.fast(G)

# You can also use a PESA-II method (experimental)
partition = re_mocd.from_nx(G)

# Check modularity
mod = re_mocd.get_modularity(G, partition)

From an Edge List File

Prepare an edge list file formatted as:

0,1,{'weight': 4}
0,2,{'weight': 5}
0,3,{'weight': 3}
...
0,10,{'weight': 2}

The weight attribute is optional and can be omitted ({}). Save your networkx graph as an edge list:

nx.write_edgelist(G, file_path, delimiter=",", data=False)

Run the algorithm:

import re_mocd

edgelist_file = "my.edgelist"
partition = re_mocd.from_file(edgelist_file)

Examples

• Plotting Example
• Comparison with Other Algorithms
• Modularity ring problem
• Single file test

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Running from Scratch

Build and Run

1. Clone the repository:

   git clone https://github.com/0l1ve1r4/re_mocd
   cd re_mocd
   

2. Compile and execute the algorithm:

   cargo run --release mygraph.edgelist
   

Debug Mode

Use the -d flag for additional debug output:

cargo run --release mygraph.edgelist -d

Debug mode helps troubleshoot and monitor the algorithm's progress effectively.

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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