activity
20032025
most citedMaximum likelihood: extracting unbiased information from complex networks

195 citations · 872 across the 35 of their papers we have counts for

collaborators

59 papers

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…

cond-mat.stat-mech2024★ 12 cited

Network Renormalization

Andrea Gabrielli, Diego Garlaschelli, Subodh P. Patil +1

The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, al…

physics.soc-ph2024★ 1 cited

Inferring firm-level supply chain networks with realistic systemic risk from industry sector-level data

Massimiliano Fessina, Giulio Cimini, Tiziano Squartini +3

Production networks constitute the backbone of every economic system. They are inherently fragile as several recent crises clearly highlighted. Estimating the system-wide consequen…

physics.soc-ph2024★ 4 cited

Patterns of link reciprocity in directed, signed networks

Anna Gallo, Fabio Saracco, Renaud Lambiotte +2

Most of the analyses concerning signed networks have focused on the balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative e…

physics.soc-ph2024★ 5 cited

Assessing frustration in real-world signed networks: a statistical theory of balance

Anna Gallo, Diego Garlaschelli, Tiziano Squartini

According to the so-called strong version of structural balance theory, actors in signed social networks avoid establishing triads with an odd number of negative links. Generalisin…

physics.soc-ph2024

Geometry-free renormalization of directed networks: scale-invariance and reciprocity

Margherita Lalli, Diego Garlaschelli

Recent research has tried to extend the concept of renormalization, which is naturally defined for geometric objects, to more general networks with arbitrary topology. The current…