Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
LCP
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"
Image for the paper "Laplacian renormalization group for heterogeneous networks"

Network renormalization

Statistical physics

Laplacian renormalization group for heterogeneous networks

Nature Physics 19, 445 (2023)

P. Villegas, T. Gili, G. Caldarelli, A. Gabrielli

The renormalisation group is a powerful tool for examining organisational scales in dynamical systems. But applying it to complex networks presents challenges because of correlations between intertwined scales. We develop a Laplacian renormalisation group approach that can identify proper spatiotemporal scales in complex networks, introducing so-called Kadanoff supernodes to resolve detrimental small-world effects.

Nature Physics 19, 445 (2023)

P. Villegas, T. Gili, G. Caldarelli, A. Gabrielli