netbone 0.2.3

A python package for extracting network backbones from simple weighted networks

NetBone

NetBone is a Python library for extracting network backbones from simple weighted networks.

Features

• Extract network backbones from simple weighted networks
• Contains six statistical methods for extracting backbones
• Contains thirteen structural methods for extracting backbones
• Contains one hybrid method for extracting backbones
• Includes three filters for extracting the backbone: boolean filter, threshold filter, and fraction filter
• Built-in methods for comparing backbones with each other
• Option to use custom comparison methods
• Visualization tools to display the results of the comparison

Installation

You should have Python version 3.10 or higher. Then you can install the latest version of NetBone:

pip install netbone

or

pip install git+https://gitlab.liris.cnrs.fr/coregraphie/netbone

Usage/Examples

To see a more detailed example, please refer to the example notebook here. However, here is a simple example using a backbone extraction method and three filters that are available:

import netbone as nb
import networkx as nx
from netbone.filters import boolean_filter, threshold_filter, fraction_filter

# load the network
g = nx.les_miserables_graph()

# apply the choosen backbone extraction method
b = nb.high_salience_skeleton(g)

# extract the backbone based on the default threshold
backbone1 = boolean_filter(b)

# extract the backbone based on a threshold(0.7)
backbone2 = threshold_filter(b, 0.7)

# extract the backbone keeping a fraction of edges(0.15)
backbone3 = fraction_filter(b, 0.15)

Citation

Ali Yassin, Abbas Haidar, Hocine Cherifi et al. An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks, 19 May 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2935871/v1]

Credits

This project includes code from the following sources:

ECM filter, Doubly Stochastic, Marginal Likelihood, Metric Distance, Ultrametric Distance

Contributing

Contributions are always welcome!

License

MIT