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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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Journal of Physics: Complexity |
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| 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. |
| format | Article |
| id | doaj-art-552300bd41df43a2832e1d30f4e01b3e |
| institution | Kabale University |
| issn | 2632-072X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| 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 |
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