Fitness centrality: a non-linear centrality measure for complex networks

As often happens in science, tools, and methods originally developed in one field can unexpectedly become useful in others. This paper explores the formalism of Economic Fitness Complexity (EFC), initially designed to predict and explain the economic trajectories of countries, cities, and regions, w...

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Main Authors: Vito D P Servedio, Alessandro Bellina, Emanuele Calò, Giordano De Marzo
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Journal of Physics: Complexity
Subjects:
Online Access:https://doi.org/10.1088/2632-072X/ada845
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author Vito D P Servedio
Alessandro Bellina
Emanuele Calò
Giordano De Marzo
author_facet Vito D P Servedio
Alessandro Bellina
Emanuele Calò
Giordano De Marzo
author_sort Vito D P Servedio
collection DOAJ
description As often happens in science, tools, and methods originally developed in one field can unexpectedly become useful in others. This paper explores the formalism of Economic Fitness Complexity (EFC), initially designed to predict and explain the economic trajectories of countries, cities, and regions, which has also proven applicable in diverse contexts such as ecology and chess openings. The success of EFC is attributed to its ability to indirectly assess hidden capabilities within a system. However, existing EFC algorithms are constrained to bipartite graphs, becoming inapplicable even with minor deviations in the bipartite structure. This paper introduces an extension of EFC and its cousin Economic Complexity Index that applies to any graph, thereby overcoming the bipartite constraint. This extension introduces fitness centrality, a novel centrality measure that can be used for assessing node vulnerability. By broadening the scope of economic complexity analysis to diverse network structures, this work expands the applicability and robustness of EFC in complexity science.
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issn 2632-072X
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publishDate 2025-01-01
publisher IOP Publishing
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series Journal of Physics: Complexity
spelling doaj-art-552300bd41df43a2832e1d30f4e01b3e2025-01-23T12:24:03ZengIOP PublishingJournal of Physics: Complexity2632-072X2025-01-016101500210.1088/2632-072X/ada845Fitness centrality: a non-linear centrality measure for complex networksVito D P Servedio0https://orcid.org/0000-0003-3221-5973Alessandro Bellina1https://orcid.org/0009-0004-3971-8025Emanuele Calò2https://orcid.org/0009-0007-5545-907XGiordano De Marzo3https://orcid.org/0000-0002-3127-5336Complexity Science Hub , Metternichgasse 8, 1030 Vienna, AustriaCentro Ricerche Enrico Fermi , Piazza del Viminale, 1, I-00184 Rome, Italy; Sony Computer Science Laboratories—Rome, Joint Initiative CREF-SONY, Centro Ricerche Enrico Fermi , Via Panisperna 89/A, 00184 Rome, Italy; Sapienza University of Rome, Physics Dept. , P.le A. Moro, 2, I-00185 Rome, ItalyIMT School for Advanced Studies Lucca , P.zza San Francesco 19, 55100 Lucca, ItalyComplexity Science Hub , Metternichgasse 8, 1030 Vienna, Austria; Centro Ricerche Enrico Fermi , Piazza del Viminale, 1, I-00184 Rome, Italy; University of Konstanz , Universitaetstrasse 10, 78457 Konstanz, GermanyAs often happens in science, tools, and methods originally developed in one field can unexpectedly become useful in others. This paper explores the formalism of Economic Fitness Complexity (EFC), initially designed to predict and explain the economic trajectories of countries, cities, and regions, which has also proven applicable in diverse contexts such as ecology and chess openings. The success of EFC is attributed to its ability to indirectly assess hidden capabilities within a system. However, existing EFC algorithms are constrained to bipartite graphs, becoming inapplicable even with minor deviations in the bipartite structure. This paper introduces an extension of EFC and its cousin Economic Complexity Index that applies to any graph, thereby overcoming the bipartite constraint. This extension introduces fitness centrality, a novel centrality measure that can be used for assessing node vulnerability. By broadening the scope of economic complexity analysis to diverse network structures, this work expands the applicability and robustness of EFC in complexity science.https://doi.org/10.1088/2632-072X/ada845complex networkseconomic complexity Indexeconomic fitness complexity
spellingShingle Vito D P Servedio
Alessandro Bellina
Emanuele Calò
Giordano De Marzo
Fitness centrality: a non-linear centrality measure for complex networks
Journal of Physics: Complexity
complex networks
economic complexity Index
economic fitness complexity
title Fitness centrality: a non-linear centrality measure for complex networks
title_full Fitness centrality: a non-linear centrality measure for complex networks
title_fullStr Fitness centrality: a non-linear centrality measure for complex networks
title_full_unstemmed Fitness centrality: a non-linear centrality measure for complex networks
title_short Fitness centrality: a non-linear centrality measure for complex networks
title_sort fitness centrality a non linear centrality measure for complex networks
topic complex networks
economic complexity Index
economic fitness complexity
url https://doi.org/10.1088/2632-072X/ada845
work_keys_str_mv AT vitodpservedio fitnesscentralityanonlinearcentralitymeasureforcomplexnetworks
AT alessandrobellina fitnesscentralityanonlinearcentralitymeasureforcomplexnetworks
AT emanuelecalo fitnesscentralityanonlinearcentralitymeasureforcomplexnetworks
AT giordanodemarzo fitnesscentralityanonlinearcentralitymeasureforcomplexnetworks