most citedA Bayesian approach to out-of-sample network reconstruction

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2026

Multiscale Renormalization of Weighted Networks

Mattia Marzi, Frank P. Pijpers, Diego Garlaschelli

Recent progress in network renormalization has identified various ways to transform network representations consistently across resolution levels. In particular, the renormalizatio…

physics.soc-ph2026

Multiscale Reconstruction of Weighted Networks from Coarse-Grained Data

Mattia Marzi, Frank P. Pijpers, Diego Garlaschelli

Network reconstruction from partial information is usually performed at the same resolution level at which constraints are observable. This becomes problematic when only coarse-gra…

physics.soc-ph2026★ 1 cited

A Bayesian approach to out-of-sample network reconstruction

Mattia Marzi, Tiziano Squartini

Networks underpin systems that range from finance to biology, yet their structure is often only partially observed. Current reconstruction methods typically fit the parameters of a…

econ.GN2026

The hidden structure of innovation networks

Lorenzo Emer, Anna Gallo, Mattia Marzi +3

Innovation emerges from complex collaboration patterns - among inventors, firms, or institutions. However, not much is known about the overall mesoscopic structure around which inv…

physics.soc-ph2025

Reproducing the first and second moments of empirical degree distributions

Mattia Marzi, Francesca Giuffrida, Diego Garlaschelli +1

The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained incre…