Real-Space Renormalization Group for Spectral Properties of Hierarchical Networks

TL;DR

This paper develops renormalization group methods to analyze spectral properties of hierarchical networks, specifically deriving the Laplacian determinant for Hanoi networks to understand their complexity and dynamic behaviors.

Contribution

It introduces a novel real-space renormalization group approach to compute spectral properties and graph complexity of hierarchical networks, enabling future physics applications.

Findings

Derived the Laplacian determinant for Hanoi networks

Identified how design modifications affect network complexity

Established recursion equations for spectral properties

Abstract

We derive the determinant of the Laplacian for the Hanoi networks and use it to determine their number of spanning trees (or graph complexity) asymptotically. While spanning trees generally proliferate with increasing average degree, the results show that modifications within the basic patterns of design of these hierarchical networks can lead to significant variations in their complexity. To this end, we develop renormalization group methods to obtain recursion equations from which many spectral properties can be obtained. This provides the basis for future applications to explore the physics of several dynamic processes.