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/).