Model for the growth of the world airline network

Verma, Trivik; Araújo, Nuno A. M.; Nagler, Jan; Andrade, José S.; Herrmann, Hans J.

doi:10.1142/s0129183116501412

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body 1 2 3 4 . Advances in the study of networks have led to important progress in understanding resilience and controllability in terms of the interaction between topology and various underlying spreading dynamics 5 6 7 8 9 10 .…”

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confidence: 99%

Failure and recovery in dynamical networks

Böttcher

Luković

Nagler

et al. 2017

Sci Rep

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Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network’s components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model’s control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks.

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confidence: 99%

Failure and recovery in dynamical networks

Böttcher

Luković

Nagler

et al. 2017

Sci Rep

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“…Nevertheless, the spatial patterns and the structure of these regions show that the current system of airline traffic is very heterogeneous and its description and analysis through quantitative methods (regionalisation, categorisation, identification of hierarchy) is not easy (Verma et al, 2016). This spatial organisation is affected by some relatively stable factors, such as the economic situation in a particular region and its tourist appeal, and it is also affected T A B L E 2 Size and categories of world airline hubs by some variable factors, such as the prevailing political situation.…”

Section: Discussionmentioning

confidence: 99%

“…A closer look at global air transport, using the concept of functional regions and with the help of a regionalisation algorithm, has identified 11 global FARs which are relatively autonomous in the world air transport system. Nevertheless, the spatial patterns and the structure of these regions show that the current system of airline traffic is very heterogeneous and its description and analysis through quantitative methods (regionalisation, categorisation, identification of hierarchy) is not easy (Verma et al, 2016). The FARs defined in this paper can be compared to other macro-scale regionalisations of the world (e.g., De Blij & Muller, 1997;Huntington, 1996, etc.…”

Section: Discussionmentioning

confidence: 99%

Global spatial organisation of air transport: The definition of functional airline regions

Halás

Kraft

Klapka

2019

Geographical Journal

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This paper deals with the issue of functional regionalisation on the global level and uses information on air transport movements as the regionalisation criterion. The analysis is based on analogies regarding the use of traditional methods, scale, and character of input information (flows) by which functional regions are usually defined. The paper tries to verify whether these analogies are viable and whether the approach can be used to assess the spatial organisation of air transport on the global level. In order to do so, the paper discusses some aspects of the development of air transport and then defines functional airline regions and their prominent cores (i.e., hub airports). The regionalisation algorithm uses a database containing more than 48,000 routes and connecting almost 1,000 airports. The results are discussed in relation to issues concerning the spatial concentration of world air transport, the spatial patterns of global functional airline regions, and the classification of world airline hubs.

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“…Exploiting the natural sparsity of the vectors, the pioneering work has applied EEM to achieve a successful reconstruction in scale-free networks with a small fraction of hubs [25]. However, in many cases, examples of real-world networks are not characterized by scale-free [26], i.e., the collaboration network of film actors [27,28], the neural network of the worm Caenorhabditis elegans [26], the power grid of the western United States [29,30], and drug trafficking network [31], et al In addition, unique structure could be observed in world airline networks [32,33] and Apollonian networks [34][35][36], which are characterized by scale-free and also satisfies basic features of small-world. EEM for reconstructing networks characterized by other features has not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Network Reconstruction in Terms of the Priori Structure Information

Guo

Yang

et al. 2021

Front. Phys.

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In this paper, we investigate the reconstruction of networks based on priori structure information by the Element Elimination Method (EEM). We firstly generate four types of synthetic networks as small-world networks, random networks, regular networks and Apollonian networks. Then, we randomly delete a fraction of links in the original networks. Finally, we employ EEM, the resource allocation (RA) and the structural perturbation method (SPM) to reconstruct four types of synthetic networks with 90% priori structure information. The experimental results show that, comparing with RA and SPM, EEM has higher indices of reconstruction accuracy on four types of synthetic networks. We also compare the reconstruction performance of EEM with RA and SPM on four empirical networks. Higher reconstruction accuracy, measured by local indices of success rates, could be achieved by EEM, which are improved by 64.11 and 47.81%, respectively.

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Model for the growth of the world airline network

Cited by 6 publications

References 19 publications

Failure and recovery in dynamical networks

Failure and recovery in dynamical networks

Global spatial organisation of air transport: The definition of functional airline regions

Network Reconstruction in Terms of the Priori Structure Information

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