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Nestedness in complex networks: Observation, emergence, and implications

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  • Manuel Sebastian Mariani
  • Zhuo-Ming Ren
  • Jordi Bascompte
  • Claudio Juan Tessone

Abstract

The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks call for an explanation of their emergence and an understanding of their potential systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness can be described as the tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Known since more than $80$ years in biogeography, nestedness has been found in systems as diverse as ecological mutualistic organizations, world trade, inter-organizational relations, among many others. This review article focuses on three main pillars: the existing methodologies to observe nestedness in networks; the main theoretical mechanisms conceived to explain the emergence of nestedness in ecological and socio-economic networks; the implications of a nested topology of interactions for the stability and feasibility of a given interacting system. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. Nestedness was found to emerge both in bipartite networks and, more recently, in unipartite ones; this review is the first comprehensive attempt to unify both streams of studies, usually disconnected from each other. We believe that the truly interdisciplinary endeavour -- while rooted in a complex systems perspective -- may inspire new models and algorithms whose realm of application will undoubtedly transcend disciplinary boundaries.

Suggested Citation

  • Manuel Sebastian Mariani & Zhuo-Ming Ren & Jordi Bascompte & Claudio Juan Tessone, 2019. "Nestedness in complex networks: Observation, emergence, and implications," Papers 1905.07593, arXiv.org.
  • Handle: RePEc:arx:papers:1905.07593
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    Cited by:

    1. Ye, Yucheng & Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan, 2022. "Forecasting countries' gross domestic product from patent data," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    2. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Zhuo-Ming Ren & An Zeng & Yi-Cheng Zhang, 2020. "Bridging nestedness and economic complexity in multilayer world trade networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    4. Wang, Xiangrong & Peron, Thomas & Dubbeldam, Johan L.A. & Kéfi, Sonia & Moreno, Yamir, 2023. "Interspecific competition shapes the structural stability of mutualistic networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    5. Michel Alexandre & Felipe Jordão Xavier & Thiago Christiano Silva & Francisco A. Rodrigues, 2022. "Nestedness in the Brazilian Financial System," Working Papers Series 566, Central Bank of Brazil, Research Department.
    6. Luciano Pietronero & Andrea Gabrielli & Andrea Zaccaria, 2019. "Economic Complexity: why we like "Complexity weighted diversification"," Papers 1912.10955, arXiv.org.
    7. Matteo Bruno & Dario Mazzilli & Aurelio Patelli & Tiziano Squartini & Fabio Saracco, 2023. "Inferring comparative advantage via entropy maximization," Papers 2304.12245, arXiv.org.
    8. Patelli, Aurelio & Napolitano, Lorenzo & Cimini, Giulio & Gabrielli, Andrea, 2023. "Geography of science: Competitiveness and inequality," Journal of Informetrics, Elsevier, vol. 17(1).
    9. Olaizola, By Norma & Valenciano, Federico, 2021. "Efficiency and stability in the connections model with heterogeneous nodes," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 490-503.
    10. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.
    11. Sabrina Aufiero & Giordano De Marzo & Angelica Sbardella & Andrea Zaccaria, 2023. "Mapping job complexity and skills into wages," Papers 2304.05251, arXiv.org.
    12. Lafond, François & Astudillo-Estévez, Pablo & Bacilieri, Andrea & Borsos, András, 2023. "Firm-level production networks: what do we (really) know?," INET Oxford Working Papers 2023-08, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    13. Aurelio Patelli & Andrea Zaccaria & Luciano Pietronero, 2021. "Universal Database for Economic Complexity," Papers 2110.00302, arXiv.org.
    14. Lin, Jian-Hong & Marchese, Emiliano & Tessone, Claudio J. & Squartini, Tiziano, 2022. "The weighted Bitcoin Lightning Network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    15. Yang, Shuhui & Li, Zhongkai & Zhou, Jianlin & Gao, Yancheng & Cui, Xuefeng, 2024. "Evolving patterns of agricultural production space in China: A network-based approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 5(1), pages 121-134.
    16. Saint‐Clair Chabert‐Liddell & Pierre Barbillon & Sophie Donnet, 2022. "Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(2), March.
    17. Angelica Sbardella & Andrea Zaccaria & Luciano Pietronero & Pasquale Scaramozzino, 2021. "Behind the Italian Regional Divide: An Economic Fitness and Complexity Perspective," LEM Papers Series 2021/30, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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