louvain-numba 0.0.1

A package for fast community detection in graphs using the Louvain method with Numba optimization.

Louvain-Numba

Louvain-Numba is a Python library for community detection in graphs using the Louvain method with Numba optimization. This library leverages Numba to accelerate the community detection process, making it suitable for large-scale graphs.

Features

• Efficient community detection using the Louvain method
• Optimized with Numba for high performance
• Supports various graph input types (NetworkX, NumPy arrays, SciPy sparse matrices)
• Generates hierarchical community structures

Installation

You can install Louvain-Numba via pip:

pip install louvain-numba

Or using Poetry:

poetry add louvain-numba

Usage

Basic Usage

import networkx as nx
import louvain_numba as lvn

# Create a random graph
G = nx.random_partition_graph([100, 100, 100], 0.1, 0.01)

# Find the best partition
partition = lvn.best_partition(G)
print("Best partition:", partition)

# Calculate modularity
modularity = lvn.modularity(partition, G)
print("Modularity:", modularity)

Specify prefered number of clusters

import networkx as nx
import louvain_numba as lvn

# Create a random graph
G = nx.random_partition_graph([100, 100, 100], 0.1, 0.01)

# Find the best partition
partition = lvn.best_partition(G, n_clusters=(7, 10))
print("Best partition:", partition)

# Calculate modularity
modularity = lvn.modularity(partition, G)
print("Modularity:", modularity)

Full Hierarchy

import networkx as nx
import louvain_numba as lvn

# Create a random graph
G = nx.random_partition_graph([100, 100, 100], 0.1, 0.01)

# Generate full hierarchy of partitions
for level, (partition, modularity) in enumerate(lvn.find_partitions(G, return_modularity=True)):
    print(f"Level {level}:")
    print("Partition:", partition)
    print("Modularity:", modularity)

Contributing

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on GitHub. License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

This library was inspired by the cylouvain package and leverages Numba for performance optimization.