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Community Detection of Chinese Airport Delay Correlation Network

Abstract : Network science has been a promising tool for characterizing and understanding complex systems. A challenging problem in network science is to uncover the community structure of the network. Community structure generally presents the partition of the nodes in the network into several groups based on various structural properties or dynamic behavior. In this paper, we analyze the community structure of Chinese airport network based on Stochastic Block Models (SBM). Different from exisiting studies, the Chinese Airport Delay Correlation Network (CADCN) is constructed with airports as nodes and the correlations between hourly delay time series of airport pairs as edges. To analyze the temporal patterns of community structures, we employ spectral clustering method and classify Chinese airports into 6 different communities. Airports within each community have closer relationships to each other on the delay propagation. A similar investigation to the traditional Chinese airport network (CAN) is carried out based on SBM as well. By comparing the results of two networks, we find that the CADCN has the advantage in revealing the implicit delay correlation than the directed flights connection between airports. Our findings have potential meanings to understand the spread of flight delays and to develop relevant management and control strategies.
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Contributor : Laurence Porte <>
Submitted on : Monday, May 11, 2020 - 10:35:04 AM
Last modification on : Friday, July 10, 2020 - 4:02:57 PM



Shuwei Chen, Yanjun Wang, Minghua Hu, Ying Zhou, Daniel Delahaye, et al.. Community Detection of Chinese Airport Delay Correlation Network. AIDA-AT 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation, Feb 2020, Singapore, Singapore. pp.1-8, ⟨10.1109/AIDA-AT48540.2020.9049192⟩. ⟨hal-02569201⟩



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