Laplacian Renormalization Group for heterogeneous networks
Statistical Mechanics
2023-01-11 v1 Disordered Systems and Neural Networks
Adaptation and Self-Organizing Systems
Biological Physics
Abstract
The renormalization group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. To date, the explorations are based on hidden geometries hypotheses. Here, we propose a Laplacian RG diffusion-based picture in complex networks, defining both the Kadanoff supernodes' concept, the momentum space procedure, \emph{\'a la Wilson}, and applying this RG scheme to real networks in a natural and parsimonious way.
Cite
@article{arxiv.2203.07230,
title = {Laplacian Renormalization Group for heterogeneous networks},
author = {Pablo Villegas and Tommaso Gili and Guido Caldarelli and Andrea Gabrielli},
journal= {arXiv preprint arXiv:2203.07230},
year = {2023}
}
Comments
7 pages, 4 figures, and Supplementary Information