backbone (version 2.0.0)

bicm: Bipartite Configuration Model

Description

bicm estimates cell probabilities under the bipartite configuration model

Usage

bicm(M, tol = 1e-08, max_steps = 200, ...)

Arguments

M

matrix: a binary matrix

tol

numeric, tolerance of algorithm

max_steps

numeric, number of times to run loglikelihood_prime_bicm algorithm

...

optional arguments

Value

matrix: a matrix of probabilities

Details

Given a binary matrix M, the Bipartite Configuration Model (BiCM; Saracco et. al. 2015) returns a valued matrix B in which Bij is the approximate probability that Mij = 1 in the space of all binary matrices with the same row and column marginals as M. The BiCM yields the closest approximations of the true probabilities compared to other estimation methods (Neal et al., 2021), and is used by sdsm() to extract the backbone of a bipartite projection using the stochastic degree sequence model.

References

Saracco, F., Di Clemente, R., Gabrielli, A., & Squartini, T. (2015). Randomizing bipartite networks: The case of the World Trade Web. Scientific Reports, 5, 10595. 10.1038/srep10595

Neal, Z. P., Domagalski, R., and Sagan, B. (2021). Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections. Scientific Reports. 10.1038/s41598-021-03238-3

Examples

# NOT RUN {
M <- matrix(rbinom(25,1,0.5),5,5)  #A random bipartite graph
bicm(M)
# }