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Can we neglect the multi-layer structure of functional networks?

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  • Zanin, Massimiliano

Abstract

Functional networks, i.e. networks representing dynamic relationships between the components of a complex system, have been instrumental for our understanding of, among others, the human brain. Due to limited data availability, the multi-layer nature of numerous functional networks has hitherto been neglected, and nodes are endowed with a single type of links even when multiple relationships coexist at different physical levels. A relevant problem is the assessment of the benefits yielded by studying a multi-layer functional network, against the simplicity guaranteed by the reconstruction and use of the corresponding single layer projection. Here, I tackle this issue by using as a test case, the functional network representing the dynamics of delay propagation through European airports. Neglecting the multi-layer structure of a functional network has dramatic consequences on our understanding of the underlying system, a fact to be taken into account when a projection is the only available information.

Suggested Citation

  • Zanin, Massimiliano, 2015. "Can we neglect the multi-layer structure of functional networks?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 184-192.
    • Handle: RePEc:eee:phsmap:v:430:y:2015:i:c:p:184-192
    DOI: 10.1016/j.physa.2015.02.099
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    Cited by:

    1. Cumelles, Joel & Lordan, Oriol & Sallan, Jose M., 2021. "Cascading failures in airport networks," Journal of Air Transport Management, Elsevier, vol. 92(C).
    2. Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor & Suau-Sanchez, Pere, 2017. "Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 119-145.
    3. Sun, Xiaoqian & Wandelt, Sebastian & Hansen, Mark & Li, Ang, 2017. "Multiple airport regions based on inter-airport temporal distances," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 84-98.
    4. Li, Qiang & Jing, Ranzhe, 2021. "Characterization of delay propagation in the air traffic network," Journal of Air Transport Management, Elsevier, vol. 94(C).
    5. Chen, Shenwen & Du, Wenbo & Liu, Runran & Cao, Xianbin, 2023. "Finding spatial and temporal features of delay propagation via multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    6. Wang, Yanjun & Zhang, Qiqian & Zhu, Chenping & Hu, Minghua & Duong, Vu, 2016. "Human activity under high pressure: A case study on fluctuation scaling of air traffic controller’s communication behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 151-157.
    7. Charley Presigny & Marie-Constance Corsi & Fabrizio De Vico Fallani, 2024. "Node-layer duality in networked systems," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    8. Bombelli, Alessandro & Sallan, Jose Maria, 2023. "Analysis of the effect of extreme weather on the US domestic air network. A delay and cancellation propagation network approach," Journal of Transport Geography, Elsevier, vol. 107(C).
    9. Lei, Lixing & Yang, Junzhong, 2021. "Patterns in coupled FitzHugh–Nagumo model on duplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    10. Belkoura, Seddik & Cook, Andrew & Peña, José Maria & Zanin, Massimiliano, 2016. "On the multi-dimensionality and sampling of air transport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 95-109.
    11. Sun, Xiaoqian & Wandelt, Sebastian & Zhang, Anming, 2022. "Ghostbusters: Hunting abnormal flights in Europe during COVID-19," Transport Policy, Elsevier, vol. 127(C), pages 203-217.

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