Language: Python 3
Last update: May 2026
Networks: monopartite, undirected, binary
Null models: Fitness Model, Fitness-Corrected Planted Partition Model
Github: https://github.com/DucDuong92/Network-reconstruction-Jeffrey
Paper: Minh Duc Duong and Diego Garlaschelli, “Network Reconstruction via Jeffreys Prior under Missing Sufficient Statistics" (https://arxiv.org/abs/2604.05745).
Notes: This is a Python package for reconstructing binary economic networks from limited aggregate information. It implements the standard Fitness Model, the Fitness-Corrected Planted Partition Model, and a Jeffreys-prior reconstruction method for cases in which block-specific sufficient statistics are missing. The package constructs the feasible parameter curve satisfying the observed total number of links, samples it uniformly in Jeffreys arclength, and identifies entropy-based representative solutions such as the median-entropy point. The package is designed as companion software for the paper and includes example notebooks for synthetic and real-data workflows.

Language: Python 3
Last update: February 2024
Networks: monopartite, undirected, dynamical
Null models: UBCM with persistent degrees
Github: https://github.com/VirginioClemente/Temporal-networks-with-node-specific-memory
Paper: Giulio Virginio Clemente, Claudio J. Tessone, and Diego Garlaschelli. "Temporal networks with node-specific memory: unbiased inference of transition probabilities, relaxation times and structural breaks" (https://arxiv.org/abs/2311.16981).
Notes: This repository contains the code for the maximum entropy configuration model with additional temporal constraints (persistent links, persistent degrees, persistent edges).

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.

Language: Python(>=3.5)
Last update: February 2021
Networks: monopartite: undirected, directed,  weighted, directed AND weighted;
bipartite undirected
Null models: UBCM, DBCM, BiCM, UECM, DECM, CReM
Pypi: https://pypi.org/project/NEMtropy/
Github:  https://github.com/nicoloval/NEMtropy
ReadTheDocs: https://nemtropy.readthedocs.io/en/master/index.html
Paper: Nicolò Vallarano et al. (2021), https://arxiv.org/pdf/2101.12625.pdf
Notes: the bipartite submodule contains also the validated projection described in Fabio Saracco et al. (2017) New J. Phys. 19 053022

Language: Python 2
Last update: 24 August 2017
Networks: bipartite, undirected
Null-model: BiCM
Paper: Fabio Saracco et al. (2015) Sci. Rep. 5 10595
Notes: the code can perform the projection from
Fabio Saracco et al. (2017) New J. Phys. 19 053022

Language: Python 3.5
Last update: 3 December 2018
Networks: binary, directed and undirected
Null-model: hypergeometric for bimodular structures
Paper: J. van Lidth de Jeude et al. (2019) EPL 125, 6
Notes: method to detect statistically significant bimodular structures, i.e. either bipartite or core-periphery ones. It is based on a modification of the so-called "surprise" metric proposed for detecting communities in networks. The present variant allows for bimodular node partitions to be revealed, by letting links to be placed either 1) within the core part and between the core and the periphery parts or 2) just between the (empty) layers of a bipartite network.

Language: C++
Last update: July 2014
Networks: monopartite, directed/undirected, weighted/unweighted
Null models: UBCM, UWCM, DBCM, DWCM, RBCM, RWCM, UECM
Paper: Tiziano Squartini et al. (2015) New J. Phys. 17 023052