collaborators

10 papers

math.PR2026

Renormalisation of Inhomogeneous Random Graphs

Luca Avena, Diego Garlaschelli, Rajat Subhra Hazra +1

We consider inhomogeneous random graphs in which vertices are assigned i.i.d.\ random weights, pairs of distinct vertices are connected by an edge independently with a probability…

physics.soc-ph2026

q-Exponential Random Graphs: higher-order networks from simple constraints

David Dobáš, Diego Garlaschelli, Petr Jizba

Exponential Random Graphs (ERGs) are among the most widely used network models, derived as principled least-bias graph ensembles that maximize Shannon entropy under constraints on…

physics.soc-ph2026

Community detection in subject-subject networks from psychometrics data

Arianna Armanetti, Luca Cecchetti, Eiko Fried +2

Identifying subgroups of respondents in psychometric data is traditionally addressed with Latent Class Analysis, which requires the number of classes to be specified a priori and c…

physics.data-an2026

Inverse generalised spin models of answers to questionnaires

Arianna Armanetti, Luca Cecchetti, Paolo Sarti +2

Network psychometrics conceptualises psychological constructs as emergent properties of systems of interacting items. Energy-based probabilistic models have gained popularity as mo…

nlin.AO2026

GDP-Driven Structural and Dynamical Heterogeneity in the Synchronization of Chaotic Macroeconomic Networks

Thierry Njougouo, Fernando Fagundes Ferreira, Diego Garlaschelli

We investigate the emergence of synchronization in a network of coupled chaotic macroeconomic systems. Each node represents an economy characterized by three key variables savings,…

math.PR2026

Clustering without geometry in sparse networks with independent edges

Alessio Catanzaro, Remco van der Hofstad, Diego Garlaschelli

The coexistence of sparsity and clustering (non-vanishing average fraction of triangles per node) is one of the few structural features that, irrespective of finer details, are ubi…