Maximum Entropy
Hub
The hub of maximum-entropy null models
for network randomization
by Tancredi Caruso, Giulio Virginio Clemente,
Matthias C Rillig, Diego Garlaschelli
Language: Python, R
Last update: August 2022
Networks: bipartite binary, bipartite weighted
Null models: BiCM, WBiCM
FigShare: this URL
Paper: Tancredi Caruso, Giulio Virginio Clemente, Matthias C Rillig, Diego Garlaschelli, Methods Ecol Evol. 2022;13:2306–2317
Notes: Methods to fit (binary and weighted) bipartite configuration models, especially designed for ecological networks.
Fit_BiCM.py & Bipartite.R:
Fitting Bipartite Configuration Models
(binary and weighted, for Python and R)
This repository (especially designed for ecological networks) contains:
- the Python script “Fit_BiCM.py” to fit a binary configuration model to a bipartite network
- the Python script “Fit_WBiCM.py” to fit a weighted, bipartite configuration model but that can also account for information on the degree sequence
- the R code Bipartite.R to analyse the data contained in the folder "samples" and generated by Fit_BiCM.py applied to the data in "bicm_mat.csv"
- the R Code Bipartite_W.R to analyse the data contained in the folder "samples_W" and generated by Fit_WBiCM.py applied to the data in "bicm_matW.csv"
- the Guideline document "Bipartite Binary Configuration model.docx" that explains the flowork to fit a binary configuration model to a bipartite network using the code Fit_BiCM.py, which uses the existing (and tested) Python package bicm2 (https://pypi.org/project/bicm/)
- The Guideline document "Bipartite Weighted Configuration model.docx" that explains the flowork to fit a weighted configuration model to a weighted bipartite network using the code “Fit_WBiCM.py”, which uses the existing (and tested) Python package NEMtropy (https://pypi.org/project/NEMtropy/).