rnbrw 0.1.2

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

RNBRW

RNBRW (Renewal Non-Backtracking Random Walks) is a Python package for estimating edge-level importance in networks using random walks that restart upon cycle closure. These weights can be used to improve community detection algorithms like Louvain.

Based on:

Moradi, B., Shakeri, H., Poggi-Corradini, P., & Higgins, M.
New methods for incorporating network cyclic structures to improve community detection
arXiv:1805.07484

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Installation

pip install rnbrw

Features

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

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)

API Reference

compute_weights(G, nsim=1000, n_jobs=1, weight_attr='rnbrw_weight', seed_base=0)

Simulates RNBRW on graph G to assign edge importance scores as weights.

Parameter Type Description

Parameter	Type	Description
G	networkx.Graph	Input undirected graph
nsim	int	Number of simulations (default = 1000)
n_jobs	int	Number of parallel jobs (default = 1; -1 = all)
weight_attr	str	Name of the edge attribute to store weights
seed_base	int	Base random seed for reproducibility

detect_communities_louvain(G, weight_attr='rnbrw_weight')

Runs Louvain on G using edge weights.

Parameter Type Description

Parameter	Type	Description
G	networkx.Graph	Weighted graph with edge weights
weight_attr	str	Edge attribute name (default = 'rnbrw_weight')

normalize_edge_weights(G, weight='rnbrw_weight')

Normalizes the weights to sum to 1 across all edges.

Parameter Type Description

Parameter	Type	Description
G	networkx.Graph	Graph whose weights are to be normalized
weight	str	Edge attribute to normalize (default='rnbrw_weight')

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} } Or use the “Cite this repository” button above.

License

This project is licensed under the MIT License © 2025 Behnaz Moradi-Jamei.

Documentation

Full documentation is available at Read the Docs.