activity
20112021
most citedFrom microscopic taxation and redistribution models to macroscopic income distributions

38 citations · 242 across the 15 of their papers we have counts for

collaborators

15 papers

physics.soc-ph2021

Diagonal degree correlations vs. epidemic threshold in scale-free networks

M. L. Bertotti, G. Modanese

We prove that the presence of a diagonal assortative degree correlation, even if small, has the effect of dramatically lowering the epidemic threshold of large scale-free networks.…

physics.soc-ph2020★ 6 cited

Network rewiring in the - plane

M. L. Bertotti, G. Modanese

We generate correlated scale-free networks in the configuration model through a new rewiring algorithm which allows to tune the Newman assortativity coefficient and the average…

physics.soc-ph2019★ 34 cited

The configuration model for Barabasi-Albert networks

M. L. Bertotti, G. Modanese

We develop and test a rewiring method (originally proposed by Newman) which allows to build random networks having pre-assigned degree distribution and two-point correlations. For…

physics.soc-ph2018★ 16 cited

The Bass diffusion model on finite Barabasi-Albert networks

M. L. Bertotti, G. Modanese

Using a mean-field network formulation of the Bass innovation diffusion model and exact results by Fotouhi and Rabbat on the degree correlations of Barabasi-Albert networks, we com…

econ.GN2017★ 2 cited

Economic inequality and mobility for stochastic models with multiplicative noise

Maria Letizia Bertotti, Amit K Chattopadhyay, Giovanni Modanese

In this article, we discuss a dynamical stochastic model that represents the time evolution of income distribution of a population, where the dynamics develop from an interplay of…

q-fin.GN2016★ 9 cited

Mathematical models describing the effects of different tax evasion behaviors

M. L. Bertotti, G. Modanese

Microscopic models describing a whole of economic interactions in a closed society are considered. The presence of a tax system combined with a redistribution process is taken into…