Connectivity and concentration in airline networks: a complexity analysis of Lufthansa's network

Reggiani, Aura; Nijkamp, Peter; Cento, Alessandro

doi:10.1057/ejis.2010.11

Cited by 41 publications

(31 citation statements)

References 46 publications

(11 reference statements)

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“…3). The existence of a hub-and-spoke is a characteristic seen in many real networks, including an airline network (Reggiani et al 2010) . That is, hubs have a tendency to connect to low degree nodes.…”

Section: Tying Networkmentioning

confidence: 99%

Network Analysis of an Interactive Health Network

Aydın¹

2016

JISNVC

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Section: Tying Networkmentioning

confidence: 99%

Network Analysis of an Interactive Health Network

Aydın¹

2016

JISNVC

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“…In most networks, only distance is taken into account, and community detection is merely the end product of the algorithm. Without a clear de nition of the network, the results can lead to conclusions of fuzzy communities and so-called unstable nodes [17]. Anomalies may arise especially in an air transport network, such as the mismatching of low degree and high betweenness of an individual node ( [18,19]).…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines

Zhang

et al. 2019

Journal of Advanced Transportation

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In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks.

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“…Most research focused on airport networks, where each node represents one airport, and an edge exists if there is a flight between two airports (Barrat et al 2004;Holmes and Scott 2004;Newman 2004;Kurant and Thiran 2006;Bonnefoy 2008;Reggiani, Nijkamp, and Cento 2010). Guimera et al (2005) analysed the worldwide air transportation network with nodes as cities and found that the most connected cities are not necessarily the most central ones.…”

Section: Introductionmentioning

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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Connectivity and concentration in airline networks: a complexity analysis of Lufthansa's network

Cited by 41 publications

References 46 publications

Network Analysis of an Interactive Health Network

Network Analysis of an Interactive Health Network

Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines

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

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