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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)

Les Misérables original network and the extracted backbones

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

Metadata

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