# Bounding robustness in complex networks under topological changes through majorization techniques

## TL;DR

This paper develops new bounds for the Kirchhoff index, a spectral graph invariant used to measure network robustness, using majorization theory to analyze topological changes in complex networks.

## Contribution

It introduces a novel methodology employing majorization and Schur-convex functions to derive tighter bounds on the Kirchhoff index during network link modifications.

## Key findings

- New bounds improve robustness analysis accuracy
- Method effectively applies to simulated networks
- Provides insights surpassing existing literature

## Abstract

Measuring robustness is a fundamental task for analyzing the structure of complex networks. Indeed, several approaches to capture the robustness properties of a network have been proposed. In this paper we focus on spectral graph theory where robustness is measured by means of a graph invariant called Kirchhoff index, expressed in terms of eigenvalues of the Laplacian matrix associated to a graph. This graph metric is highly informative as a robustness indicator for several realworld networks that can be modeled as graphs. We discuss a methodology aimed at obtaining some new and tighter bounds of this graph invariant when links are added or removed. We take advantage of real analysis techniques, based on majorization theory and optimization of functions which preserve the majorization order (Schurconvex functions). Applications to simulated graphs show the effectiveness of our bounds, also in providing meaningful insights with respect to the results obtained in the literature.