rnbrw 0.1.0

Renewal Non-Backtracking Random Walk (RNBRW) for community detection

RNBRW

A Python package to compute Renewal Non-Backtracking Random Walk (RNBRW) edge weights for community detection.

Features

• Parallel RNBRW edge weight estimation
• Seamless integration with Louvain
• Based on Moradi-Jamei et al., 2019

Installation

pip install git+https://github.com/Behnaz-m/RNBRW.git

Usage

import networkx as nx
from rnbrw.weights import compute_weights
from rnbrw.community import detect_communities_louvain

# Create or load a graph
G = nx.karate_club_graph()

# Compute RNBRW weights
G = compute_weights(G, nsim=1000, n_jobs=4)

# Detect communities
partition = detect_communities_louvain(G)

Documentation

Full documentation is available at Read the Docs.

Citation

If you use this package in your research, please cite:

@article{moradi2018new,
	title={New methods for incorporating network cyclic structures to improve community detection},
	author={Moradi, Behnaz and Shakeri, Heman and Poggi-Corradini, Pietro and Higgins, Michael},
	journal={arXiv preprint arXiv:1805.07484},
	year={2018}
}

License

MIT