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Bounding robustness in complex networks under topological changes through majorization techniques

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Abstract

Measuring robustness is a fundamental task for analysing 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 real-world 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. Applications to simulated graphs and to empirical networks generated by collecting assets of the S&P 100 show the effectiveness of our bounds, also in providing meaningful insights with respect to the results obtained in the literature.

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Authors and Affiliations

  1. Department of Mathematics for Economic, Financial and Actuarial Sciences, Universitá Cattolica del Sacro Cuore, Milano, Italy

    Gian Paolo Clemente & Alessandra Cornaro

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  1. Gian Paolo Clemente
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  2. Alessandra Cornaro
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Correspondence to Alessandra Cornaro.

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Clemente, G.P., Cornaro, A. Bounding robustness in complex networks under topological changes through majorization techniques. Eur. Phys. J. B 93, 114 (2020). https://doi.org/10.1140/epjb/e2020-100563-2

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