Codes and datasets

My GitHub repository dcms [Work In Progress!] solves the maximum entropy null models of the Directed Configuration Model (DCM), the Directed Weighted Configuration Model (DWCM), the Directed Enhanced Configuration Model (DECM), and the quasi-Directed Enhanced Configuration Model (qDECM), aka separable DECM or Directed CREM_A.

The Python module NEMtropy,  the Python module calculating several entropy-based null-models, by Nicolò Vallarano, Emiliano Marchese, and Matteo Bruno, some "old" Ph.D. students of IMT.  The procedure for the calculation is described in "Fast and scalable likelihood maximization...";

The Python module bicm, i.e. the module dedicated to the randomization of bipartite networks, by Matteo Bruno, my last Ph.D. student at IMT, now at Sony CSL. bicm is part of NEMtropy, mentioned above. Nevertheless, as it is a standalone module, it is sometimes updated independently of NEMtropy updates. I recommend using all the bipartite tools from here, instead of passing through NEMtropy.

My GitHub repository of the lectures of the course "Python and Network". We start from the very beginning and end up by "scraping" the dataset of the exercises. Lecture 5 was provided by Prof. Angelo Facchini.

The GitHub of the Bipartite Configuration Model (BiCM) by Mika J. Straka, a Ph.D. student of mine. It is the very first version of a public repository for the randomization of bipartite networks. Among Mika's repositories, you can find those necessary for the validated projection of "Inferring..."

Most of the Twitter dataset that I analysed in the various papers can be found at the repository of the TOFFEe project, funded by IMT School For Advanced Studies Lucca.