Modelling the air transport with complex networks: A short review
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Complex networks theory has played a crucial role in the aviation system in the last two decades, facilitating efficiency and safety of air travel, particularly in identifying key components and improving air transport resilience.
“the theory of complex networks has played a crucial role in the aviation system in the last two decades, facilitating the efficiency and safety of air travel, particularly in the context of identifying key components and improving air transport resilience; see Zanin and Lillo (2013) ; Zanin and Wandelt (2023) for reviews.”
From Status quo and challenges in air transport management research (2024) · cites this paper as Zanin and Lillo (2013)
Section 5 (pp. 22–23) reviews network studies that identify vulnerable hubs and examine ways to improve resilience. But the Introduction (p. 3) and Conclusions (p. 24) describe network theory’s contribution to future challenges as a prospect, not an established improvement in air-travel efficiency or safety. The review therefore supports the analytical role claimed, but not the assertion that it has already facilitated those outcomes. (LLM, conf=0.86)
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From random failures to targeted attacks in network dismantling
Wandelt, S., Lin, W., Sun, X., & Zanin, M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108146 · Cited by 71 · see citing works
Self-citationCan we neglect the multi-layer structure of functional networks?
Zanin, M. (2015) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.02.099 · Cited by 71 · see citing works
Self-citationStudying the Topology of Transportation Systems through Complex Networks: Handle with Care
Zanin, M., Sun, X., & Wandelt, S. (2018) · Journal of Advanced Transportation · doi:10.1155/2018/3156137 · Cited by 71 · see citing works
Self-citationJanic, M. M. (2015) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2015.07.012 · Cited by 71 · see citing works
Robustness of airline route networks
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PID Control for Synchronization of Complex Dynamical Networks With Directed Topologies
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Status quo and challenges in air transport management research
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“…has played a crucial role in the aviation system in the last two decades, facilitating the efficiency and safety of air travel, particularly in the context of identifying key components and improving air transport resilience; see Zanin and Lillo (2013) ; Zanin and Wan-delt (2023) for reviews.”
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Xiao, R., Xiao, T., Zhao, P., Zhang, M., Ma, T., & Qiu, S. (2024) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2024.107102 · Cited by 61 · see citing works
Data-driven analysis of resilience in airline networks
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Computationally efficient attack design for robustness analysis of air transportation networks
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Analysis of the Air Transport Network Characteristics of Major Airports
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Network similarity analysis of air navigation route systems
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Tsiotas, D., & Polyzos, S. (2015) · Journal of Complex Networks · doi:10.1093/comnet/cnv003 · Cited by 56 · see citing works
A Crowdsourcing Approach for Sustainable Last Mile Delivery
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Multi-Scale Analysis of the European Airspace Using Network Community Detection
Gurtner, G., Vitali, S., Cipolla, M., Lillo, F., Mantegna, R. N., Miccichè, S., & Pozzi, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094414 · Cited by 55 · see citing works
Self-citationForecasting Flight Delays Using Clustered Models Based on Airport Networks
Güvercin, M., Ferhatosmanoğlu, N., & Gedik, B. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2990960 · Cited by 54 · see citing works
Air transport networks of global integrators in the more liberalized Asian air cargo industry
Malighetti, P., Martini, G., Redondi, R., & Scotti, D. (2019) · Transport Policy · doi:10.1016/j.tranpol.2019.04.021 · Cited by 53 · see citing works
Neural Network Spectral Robustness under Perturbations of the Underlying Graph
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Structural properties of the Chinese air transportation multilayer network
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Zhang, L., Lu, J. J., Fu, B.-B., & Li, S. (2018) · Complexity · doi:10.1155/2018/2156309 · Cited by 48 · see citing works
A framework for the construction of generative models for mesoscale structure in multilayer networks
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Revealing the component structure of the world air transportation network
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QRE: Quick Robustness Estimation for large complex networks
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Self-citationResilience of cities towards airport disruptions at global scale
Sun, X., Wandelt, S., & Zhang, A. (2020) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2020.100452 · Cited by 41 · see citing works
Zhu, C., Wu, J., Liu, M., Wang, L., Li, D., & Kouvelas, A. (2021) · Transport Policy · doi:10.1016/j.tranpol.2021.03.009 · Cited by 40 · see citing works
On Node Criticality in Air Transportation Networks
Sun, X., Wandelt, S., & Cao, X. (2017) · Networks and Spatial Economics · doi:10.1007/s11067-017-9342-5 · Cited by 39 · see citing works
The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience
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Measuring the development of airline networks: Comprehensive indicators
Roucolle, C., Seregina, T., & Urdánoz, M. (2020) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.12.010 · Cited by 38 · see citing works
The Impact of Network Topology on Air Transportation Robustness to Pandemics
Zhou, Y., Li, S., Kundu, T., Bai, X., & Qin, W. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3085818 · Cited by 38 · see citing works
Potential global jamming transition in aviation networks
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Wandelt, S., Zhang, A., & Sun, X. (2024) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2024.104522 · Cited by 37 · see citing works
Generation and recovery of airborne delays in air transport
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Self-citationTopological data analysis for aviation applications
Li, M. Z., Ryerson, M. S., & Balakrishnan, H. (2019) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2019.05.017 · Cited by 35 · see citing works
Measuring the cost of resilience
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Core and critical cities of global region airport networks
Lordan, O., & Sallan, J. M. (2018) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2018.08.123 · Cited by 34 · see citing works
A novel measure of edge and vertex centrality for assessing robustness in complex networks
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A new network model for extracting text keywords
Yang, L., Li, K., & Huang, H. (2018) · Scientometrics · doi:10.1007/s11192-018-2743-5 · Cited by 33 · see citing works
New centrality and causality metrics assessing air traffic network interactions
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Self-citationControl and Optimization of Air Traffic Networks
Gopalakrishnan, K., & Balakrishnan, H. (2021) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-070720-080844 · Cited by 33 · see citing works
Using inter-town network analysis in city system planning: A case study of Hubei Province in China
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Worldwide Railway Skeleton Network: Extraction Methodology and Preliminary Analysis
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Viljoen, N. M., & Joubert, J. W. (2017) · Networks and Spatial Economics · doi:10.1007/s11067-017-9370-1 · Cited by 32 · see citing works
Analysis of the Chinese provincial air transportation network
Du, W., Liang, B.-Y., Hong, C. H., & Lordan, O. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.08.067 · Cited by 32 · see citing works
Vulnerability and Resilience Analysis of the Air Traffic Control Sector Network in China
Wang, X., Miao, S., & Tang, J. (2020) · Sustainability · doi:10.3390/su12093749 · Cited by 32 · see citing works
Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making
Yang, Y., Lei, Y., Zhou, Z., Chen, Y., & Kou, T. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/9728742 · Cited by 32 · see citing works
Network analysis reveals patterns behind air safety events
Zanin, M. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.01.032 · Cited by 29 · see citing works
Self-citationChung, H. M., Kwon, O. K., Han, O. S., & Kim, H.-J. (2020) · Transport Policy · doi:10.1016/j.tranpol.2020.09.002 · Cited by 28 · see citing works
On the multi-dimensionality and sampling of air transport networks
Belkoura, S., Cook, A. J., Peña, J. M., & Zanin, M. (2016) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2016.07.013 · Cited by 28 · see citing works
Self-citationZhao, L., Cheng, Z., Li, H., & Hu, Q. (2019) · Journal of Advanced Transportation · doi:10.1155/2019/9536273 · Cited by 28 · see citing works
Flight delays in European airline networks
Bubalo, B., & Gaggero, A. A. (2021) · Research in Transportation Business & Management · doi:10.1016/j.rtbm.2021.100631 · Cited by 27 · see citing works
Modelling and analysis of transportation networks using complex networks: Poland case study
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Worldwide air transportation networks: a matter of scale and fractality?
Sun, X., Wandelt, S., & Zanin, M. (2017) · Transportmetrica A Transport Science · doi:10.1080/23249935.2017.1312632 · Cited by 26 · see citing works
Self-citationModeling resilience of the ATC (Air Traffic Control) sectors
Jakšić, Z., & Janic, M. M. (2020) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2020.101891 · Cited by 25 · see citing works
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A new generalized travel cost based connectivity metric applied to Scandinavian airports
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An empirical study of the resilience of the US and European air transportation networks
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Global air transport complex network: multi-scale analysis
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Complex network based comparative analysis of Delhi Metro network and its extension
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Air delay propagation patterns in Europe from 2015 to 2018: an information processing perspective
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Self-citationNetworks: An Introductionby M. E. J. Newman
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- Authors
- Zanin, M., & Lillo, F.
- Affiliations
- Innaxis Foundation & Research Institute (ES)
- Santa Fe Institute (US)
- Corresponding author
- Zanin, Massimiliano
- Lillo, Fabrizio
- Venue
- The European Physical Journal Special Topics
- Published
- 2013-01
- Volume
- 215
- Issue
- 1
- Pages
- 5-21
- ISSN
- 1951-6355 (ISSN-L)
- 1951-6401
- OpenAlex
- W2023841300
- Cited by
- 277
- References
- 76
- Citations by year
- 2026: 20 · 2025: 15 · 2024: 17 · 2023: 22 · 2022: 18 · 2021: 34 · 2020: 28 · 2019: 20 · 2018: 22 · 2017: 20 · 2016: 21 · 2015: 21 · 2014: 15 · 2013: 3
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The European Physical Journal Special Topics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Springer Link (Chiba Institute of Technology) (submittedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Scuola Normale Superiore di Pisa (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Social Sciences › Urban Studies › Global Urban Networks and Dynamics
- Keywords
- air transport networks, complex networks, airport networks, flight networks, network topology, network dynamics, epidemic spread
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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