The evolving network structure of US airline system during 1990–2010

Lin, Jingyi; Ban, Y.

doi:10.1016/j.physa.2014.05.040

Cited by 61 publications

(42 citation statements)

References 43 publications

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“…The authors found that the degree distribution is non-stationary and is subject to accelerated growth; the average degree increases while the average shortest path length decreases; the average clustering coefficient decreases for growing node degrees; and the average degree of nearest neighbours is constant over the time span 1979-2007. Further, Lin and Ban (2014) analysed the evolution of topological features of the US airline network from 1990 to 2010. Wang, Mo, and Wang (2014) analysed the evolution process of the air transport network of China from 1930 to 2012.…”

Section: Literature Reviewmentioning

confidence: 99%

Temporal evolution analysis of the European air transportation system: air navigation route network and airport network

Sun

Wandelt

Linke

2014

Transportmetrica B: Transport Dynamics

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Airspace is a networked space that constantly changes in order to adapt with the changing demand of air traffic. The goal of this research is to study the temporal evolution of the European air transportation system. We analyse two network layers: the air navigation route network and the airport network. For each network layer, we analyse the temporal evolution of seven centrality measures. We quantify the seasonal and weekly variation patterns by the coefficient of variation. We find that the air navigation route network is dominated by the summer/winter seasonal variations, while the airport network shows both summer/winter seasonal variations and peak/off-peak weekly patterns. From the distributions of the metrics, we find that hub nodes existing in both network layers are potentially bottlenecks of the network. Our research helps the stakeholders in air transportation systems to monitor the network performance over time and to better understand the network dynamics.

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Section: Literature Reviewmentioning

confidence: 99%

Temporal evolution analysis of the European air transportation system: air navigation route network and airport network

Sun

Wandelt

Linke

2014

Transportmetrica B: Transport Dynamics

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“…The relationship of topological measures has been a research topic for several years [4,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], and there mainly exist two paradigms, i.e., analytical modeling and data-driven modeling. For a few topological measures of model networks, i.e., networks generated with certain algorithm, some analytical interrelationships are found.…”

Section: Related Workmentioning

confidence: 99%

“…Lin and Ban focused on the evolution of the US airline system from a complex network perspective. By plotting scatter diagrams and calculating linear correlations, they found that there was a high correlation between "strength" and "degree", while "betweenness" did not always keep consistent with "degree" [28].…”

Section: Related Workmentioning

confidence: 99%

Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

2014

Entropy

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Topological measures are crucial to describe, classify and understand complex networks. Lots of measures are proposed to characterize specific features of specific networks, but the relationships among these measures remain unclear. Taking into account that pulling networks from different domains together for statistical analysis might provide incorrect conclusions, we conduct our investigation with data observed from the same network in the form of simultaneously measured time series. We synthesize a transfer entropy-based framework to quantify the relationships among topological measures, and then to provide a holistic scenario of these measures by inferring a drive-response network. Techniques from Symbolic Transfer Entropy, Effective Transfer Entropy, and Partial Transfer Entropy are synthesized to deal with challenges such as time series being nonstationary, finite sample effects and indirect effects. We resort to kernel density estimation to assess significance of the results based on surrogate data. The framework is applied to study 20 measures across 2779 records in the Technology Exchange Network, and the results are consistent with some existing knowledge. With the drive-response network, we evaluate the influence of each measure by calculating its strength, and cluster them into three classes, i.e., driving measures, responding measures and standalone measures, according to the network communities.

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“…Compared with that of water, rail, or road, the contribution of air transportation is becoming more and more necessary and important. Air transportation plays an irreplaceable role in the modern world [3]. Air transportation is a necessary means for the fast and effective movements of people and cargoes over large distances, and it is critical to the functioning of countries and the world economy.…”

Section: Introductionmentioning

confidence: 99%

“…Characterizing the temporal properties is thus of great importance in the understanding of evolution processes taking place in air networks. There are some studies focusing on the evolution of airline networks in different time-scale intervals, such as a year [3][4][5]19,21,22], a quarter [18], or a week [13]. Although some valuable insights have been provided in the literature by analyzing the structure of air networks at different time scales (e.g., annual, monthly, or daily scales), there is an increasing need to characterize the network temporal evolution at smaller scales [23].(ii) The air networks from smaller-scale perspectives.…”

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confidence: 99%

The Structure and Periodicity of the Chinese Air Passenger Network

Li¹,

Wang²,

Bai³

et al. 2018

Sustainability

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China's air transportation system is evolving with its own unique mechanism. In particular, the structural features of the Chinese air passenger network (CAPN) are of interest. This paper aims to analyze the CAPN from holistic and microcosmic perspectives. Considering that the topological structure and the capacity (i.e., available passenger-seats) flow are important to the air network's performance, the CAPN structure features from non-weighted and weighted perspectives are analyzed. Subnets extracted by time-scale constraints of one day or every two-hours are used to find the temporal features. This paper provides some valuable conclusions about the structural characteristics and temporal features of the CAPN. The results indicate that the CAPN has a small-world and scale-free structure. The cumulative degree distribution of the CAPN follows a two-regime power-law distribution. The CAPN tends to be disassortative. Some important airports, including national air-hubs and local air-hubs, remarkably affect the CAPN. About 90% of large capacities exist between airports with large degrees. The properties of CAPN subnets extracted by taking two hours as the time-scale interval shed light on the air network performance and the changing rule more accurately and microcosmically. The method of the spectral destiny estimation is used to find the implicit periodicity mathematically. For most indicators, a one-day cycle, two-day cycle, and/or three-day cycle can be found.Sustainability 2019, 11, 54 2 of 16 to have attracted adequate research interest [5,6]. Analyzing the complex network model of airports considering all flights among destination airports throughout a country or the world has been the subject of many complex network studies. For air transportation, the structure and properties of the air network play a vital role. Complex network theory is naturally a useful tool for practitioners and researchers to grasp the overall air network features and flow patterns, to identify the importance of individual airports, and so on [4].(i) The literature on complex network models of airline networks.In complex network models of airline networks, the air network is modelled as a graph that comprises airports as vertices linked by flights. In the literature, the air network structure has been investigated from world-wide or national angles [1,4,[7][8][9][10][11][12][13][14][15][16][17][18][19].The world-wide airline network (WAN) has been analyzed by its topology, as well as by the perspective of its dynamics. The majority of published studies have reached the consensus that the WAN exhibits scale-free and small-world properties [7][8][9][10][11][12]. The small-world property implies that the average topological distances between vertices increase very slowly (logarithmically or even more slowly), along with the number of vertices increasing. Research on the correlation between weight and topology indicated that the correlation follows a power-law [8,9]. To the centrality of airports, the most connected vertices on the WAN ar...

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The evolving network structure of US airline system during 1990–2010

Cited by 61 publications

References 43 publications

Temporal evolution analysis of the European air transportation system: air navigation route network and airport network

Temporal evolution analysis of the European air transportation system: air navigation route network and airport network

Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

The Structure and Periodicity of the Chinese Air Passenger Network

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