The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles

By Roger Guimerà, Stefano Mossa, A. Turtschi, Luís A. Nunes Amaral. Proceedings of the National Academy of Sciences of the United States of America, 2003

1,776 citations recorded by the source corpus.

We analyze the global structure of the worldwide air transportation network, a critical infrastructure with an enormous impact on local, national, and international economies. We find that the worldwide air transportation network is a scale-free small-world network. In contrast to the prediction of scale-free network models, however, we find that the most connected cities are not necessarily the most central, resulting in anomalous values of the centrality. We demonstrate that these anomalies arise because of the multicommunity structure of the network. We identify the communities in the air transportation network and show that the community structure cannot be explained solely based on geographical constraints and that geopolitical considerations have to be taken into account. We identify each city's global role based on its pattern of intercommunity and intracommunity connections, which enables us to obtain scale-specific representations of the network.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1f0a11211f3c7351babce1107b5d1cbf1972d870

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “Provincial hubs are connected mainly to nodes in their own modules, whereas connector hubs are connected to nodes in other modules (Guimera et al., 2005; Chen et al., 2008; He et al., 2009b).”
Exploring the network structure and nodal centrality of China (2011, background): “…edges (m) Average degree (hki) Average path length (L) Clustering coefficient (C) Network structure Bagler (2008) India 79 455 11.52 2.26 0.66 SW Guimerá et al. (2005) World 3883 27,051 13.93 4.4 0.62 SF SW Guida and Maria (2007) Italy 50 310 12.40 1.98 2.14 0.07 0.1 SF SW Fractal Xu and…”
Functional network organization of the human brain (2011, methodology): “Participation coefficients were calculated after (Guimerà et al., 2005).”
Layered complex networks. (2005, background): “This describes well a speciﬁc type of transportation network, where all traﬃc ﬂows are one-hop long and where the two layers actually coincide, such as airport networks [16, 17].”
Size reduction of complex networks preserving modularity (2007, methodology): “The networks analysed are: the Zachary’s karate club network [16], the Jazz musicians network [17], the e-mail network of the University Rovira i Virgili [18], the airports network with data about passenger flights operating in the time period 1 November 2000–31 October 2001 compiled by the Official”
Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems (2010, background): “Figures 2A-C provide thumbnail illustrations of the hierarchical structure of the journal citation network of science (20), the global air traffic network (21), and the human disease network (22).”
Network resilience (2020, background): “For instance, air transportation networks have typical modular structures due to geographical constraints and geopolitical considerations [597].”
Networks and the Epidemiology of Infectious Disease (2010, background): “The models based on the USA and Great Britain, considered a wider range of control measures, including school closures, household prophylaxis using antiviral drugs, and vaccination, and predicted the likely impact of different policies.”
Multiscale mobility networks and the spatial spreading of infectious diseases (2009, background): “Global mobility flows therefore form very complex multiscale networks (21) spanning several orders of magnitude in intensity and spatiotemporal scales ranging from the long-range intercontinental air traffic (13, 15) to the short range commuting flows (17–19).”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “The worldwide airport network [16, 252] is a scale-free, sma ll-world network.”
Analysis of the structure of complex networks at different resolution levels (2007, background): “This is the case, for example in the worldwide airports network [6], the WWW [7], biological networks [8, 9, 10], social networks [3, 11] and the Internet [12, 13], among others.”