Metadata-Version: 2.4
Name: rnbrw
Version: 0.1.0
Summary: Renewal Non-Backtracking Random Walk (RNBRW) for community detection
Home-page: https://github.com/yourusername/rnbrw
Author: Behnaz Moradi-Jamei
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: networkx
Requires-Dist: joblib
Requires-Dist: python-louvain
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 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](https://arxiv.org/abs/1805.07484)

## Installation
```bash
pip install git+https://github.com/Behnaz-m/RNBRW.git
```

## Usage
```python
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](https://rnbrw.readthedocs.io).

## 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
