The hub of maximum-entropy null models
for network randomization
by Jeroen van Lidth de Jeude
Language: Python 3.5
Last update: 11th January 2019
Networks: monopartite, directed/undirected, unweighted
Null-model: DBCM, RBCM
Papers: Jeroen van Lidth de Jeude et al. (2019) Complexity 2019 5120581
When facing the problem of reconstructing complex mesoscale network structures, it is generally believed that models encoding the nodes organization into modules must be employed. The present paper focuses on two block structures that characterize the empirical mesoscale organization of many real-world networks, i.e., the bow-tie and the core-periphery ones, with the aim of quantifying the minimal amount of topological information that needs to be enforced in order to reproduce the topological details of the former. Our analysis shows that constraining the network degree sequences is often enough to reproduce such structures, as confirmed by model selection criteria as AIC or BIC. As a byproduct, our paper enriches the toolbox for the analysis of bipartite networks, still far from being complete: both the bow-tie and the core-periphery structure, in fact, partition the networks into asymmetric blocks characterized by binary, directed connections, thus calling for the extension of a recently proposed method to randomize undirected, bipartite networks to the directed case.
(from the paper abstract)
The code for the analytically solved null models, the Directed Configuration Model and the Reciprocated Configuration Model, is provided in a jupyter notebook (for now) running on Python 3.5. This notebook contains all explanations about the method, the functions and working examples to show how to use the code.
(from the repository description)