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Package for bipartite configuration model

Project description

BiCM package.

This is a Python package for the computation of the maximum entropy bipartite configuration model (BiCM) and the projection of bipartite networks on one layer. It was developed with Python 3.5.

You can install this package via pip:

pip install bicm

Documentation is available at

For more solvers of maximum entropy configuration models visit

Basic functionalities

To install:

pip install bicm

To import the module:

import bicm

To generate a Graph object and initialize it (with a biadjacency matrix, edgelist or degree sequences):

from bicm import BipartiteGraph
myGraph = BipartiteGraph()
myGraph.set_degree_sequences((first_degree_sequence, second_degree_sequence))

Or alternatively, with the respective data structure as input:

from bicm import BipartiteGraph
myGraph = BipartiteGraph(biadjacency=my_biadjacency_matrix, adjacency_list=my_adjacency_list, edgelist=my_edgelist, degree_sequences=((first_degree_sequence, second_degree_sequence)))

To compute the BiCM probability matrix of the graph or the relative fitnesses coefficients as dictionaries containing the nodes names as keys:

my_probability_matrix = myGraph.get_bicm_matrix()
my_x, my_y = myGraph.get_bicm_fitnesses()

This will solve the bicm using recommended settings for the solver. To customize the solver you can alternatively use (in advance) the following method:

myGraph.solve_tool(light_mode=False, method='newton', initial_guess=None, tolerance=1e-8, max_steps=None, verbose=False, linsearch=True, regularise=False, print_error=True, exp=False)

To get the rows or columns projection of the graph:


Alternatively, to customize the projection:

myGraph.compute_projection(rows=True, alpha=0.05, method='poisson', threads_num=4, progress_bar=True)

See a more detailed walkthrough in tests/bicm_tests notebook or python script, or check out the API in the documentation.

How to cite

If you use the bicm module, please cite its location on Github and the original articles [Saracco2015] and [Saracco2017].


[Vallarano2021] N. Vallarano, M. Bruno, E. Marchese, G. Trapani, F. Saracco, T. Squartini, G. Cimini, M. Zanon, Fast and scalable likelihood maximization for Exponential Random Graph Models, Preprint

[Saracco2015] F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015).

[Saracco2017] F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)

[Squartini2011] T. Squartini, D. Garlaschelli, Analytical maximum-likelihood method to detect patterns in real networks, New Journal of Physics 13, (2011)

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