Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/3032015 |
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| _version_ | 1832564287518801920 |
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| author | Weiwei Wu Haoyu Zhang Shengrun Zhang Frank Witlox |
| author_facet | Weiwei Wu Haoyu Zhang Shengrun Zhang Frank Witlox |
| author_sort | Weiwei Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aef1718e12254c5f922aa944cb26f6a7 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-aef1718e12254c5f922aa944cb26f6a72025-02-03T01:11:26ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/30320153032015Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest AirlinesWeiwei Wu0Haoyu Zhang1Shengrun Zhang2Frank Witlox3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, Jiangsu, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, Jiangsu, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, Jiangsu, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, Jiangsu, ChinaIn 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.http://dx.doi.org/10.1155/2019/3032015 |
| spellingShingle | Weiwei Wu Haoyu Zhang Shengrun Zhang Frank Witlox Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines Journal of Advanced Transportation |
| title | Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines |
| title_full | Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines |
| title_fullStr | Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines |
| title_full_unstemmed | Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines |
| title_short | Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines |
| title_sort | community detection in airline networks an empirical analysis of american vs southwest airlines |
| url | http://dx.doi.org/10.1155/2019/3032015 |
| work_keys_str_mv | AT weiweiwu communitydetectioninairlinenetworksanempiricalanalysisofamericanvssouthwestairlines AT haoyuzhang communitydetectioninairlinenetworksanempiricalanalysisofamericanvssouthwestairlines AT shengrunzhang communitydetectioninairlinenetworksanempiricalanalysisofamericanvssouthwestairlines AT frankwitlox communitydetectioninairlinenetworksanempiricalanalysisofamericanvssouthwestairlines |