Laplacian Renormalization Group: An introduction to heterogeneous coarse-graining

TL;DR

This paper introduces the Laplacian Renormalization Group (LRG), a novel method for coarse-graining heterogeneous networks that extends traditional RG techniques to complex systems with multi-scale correlations.

Contribution

The paper presents the LRG as a practical framework for applying RG to complex networks, generalizing Kadanoff supernodes and analyzing network evolution under coarse-graining.

Findings

LRG effectively mitigates cross-scale correlations in small-world networks.

The method provides a rigorous momentum space formulation of RG for networks.

Network properties evolve predictably along the LRG flow.

Abstract

The renormalization group (RG) constitutes a fundamental framework in modern theoretical physics. It allows the study of many systems showing states with large-scale correlations and their classification in a relatively small set of universality classes. RG is the most powerful tool for investigating organizational scales within dynamic systems. However, the application of RG techniques to complex networks has presented significant challenges, primarily due to the intricate interplay of correlations on multiple scales. Existing approaches have relied on hypotheses involving hidden geometries and based on embedding complex networks into hidden metric spaces. Here, we present a practical overview of the recently introduced Laplacian Renormalization Group for heterogeneous networks. First, we present a brief overview that justifies the use of the Laplacian as a natural extension for well-known field theories to analyze spatial disorder. We then draw an analogy to traditional real-space renormalization group procedures, explaining how the LRG generalizes the concept of "Kadanoff supernodes" as block nodes that span multiple scales. These supernodes help mitigate the effects of cross-scale correlations due to small-world properties. Additionally, we rigorously define the LRG procedure in momentum space in the spirit of Wilson RG. Finally, we show different analyses for the evolution of network properties along the LRG flow following structural changes when the network is properly reduced.