Community detection in airline networks : an empirical analysis of American vs. Southwest airlines
- Author
- Weiwei Wu, Haoyu Zhang, Shengrun Zhang (UGent) and Frank Witlox (UGent)
- Organization
- Abstract
- 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.
- Keywords
- CENTRALITY
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8659540
- MLA
- Wu, Weiwei, et al. “Community Detection in Airline Networks : An Empirical Analysis of American vs. Southwest Airlines.” JOURNAL OF ADVANCED TRANSPORTATION, vol. 2019, 2019, doi:10.1155/2019/3032015.
- APA
- Wu, W., Zhang, H., Zhang, S., & Witlox, F. (2019). Community detection in airline networks : an empirical analysis of American vs. Southwest airlines. JOURNAL OF ADVANCED TRANSPORTATION, 2019. https://doi.org/10.1155/2019/3032015
- Chicago author-date
- Wu, Weiwei, Haoyu Zhang, Shengrun Zhang, and Frank Witlox. 2019. “Community Detection in Airline Networks : An Empirical Analysis of American vs. Southwest Airlines.” JOURNAL OF ADVANCED TRANSPORTATION 2019. https://doi.org/10.1155/2019/3032015.
- Chicago author-date (all authors)
- Wu, Weiwei, Haoyu Zhang, Shengrun Zhang, and Frank Witlox. 2019. “Community Detection in Airline Networks : An Empirical Analysis of American vs. Southwest Airlines.” JOURNAL OF ADVANCED TRANSPORTATION 2019. doi:10.1155/2019/3032015.
- Vancouver
- 1.Wu W, Zhang H, Zhang S, Witlox F. Community detection in airline networks : an empirical analysis of American vs. Southwest airlines. JOURNAL OF ADVANCED TRANSPORTATION. 2019;2019.
- IEEE
- [1]W. Wu, H. Zhang, S. Zhang, and F. Witlox, “Community detection in airline networks : an empirical analysis of American vs. Southwest airlines,” JOURNAL OF ADVANCED TRANSPORTATION, vol. 2019, 2019.
@article{8659540,
abstract = {{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.}},
articleno = {{3032015}},
author = {{Wu, Weiwei and Zhang, Haoyu and Zhang, Shengrun and Witlox, Frank}},
issn = {{0197-6729}},
journal = {{JOURNAL OF ADVANCED TRANSPORTATION}},
keywords = {{CENTRALITY}},
language = {{eng}},
pages = {{11}},
title = {{Community detection in airline networks : an empirical analysis of American vs. Southwest airlines}},
url = {{http://doi.org/10.1155/2019/3032015}},
volume = {{2019}},
year = {{2019}},
}
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