Open Access

ARTICLE

Community Detection in Aviation Network Based on K-means and Complex Network

Hang He^1,*, Zhenhan Zhao¹, Weiwei Luo¹, Jinghui Zhang²

1 Civil Aviation Flight University of China, Guanghan, 618307, China
2 WebChain Pty Ltd, Sydney, NSW 2118, Australia

* Corresponding Author: Hang He. Email:

Computer Systems Science and Engineering 2021, 39(2), 251-264. https://doi.org/10.32604/csse.2021.017296

Received 26 January 2021; Accepted 16 March 2021; Issue published 20 July 2021

Abstract

With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means clustering algorithm, five values are obtained as the initial clustering parameter K values for each of the aviation network hierarchies classified according to five complex network indicators. Meanwhile, clustering simulation experiments are conducted to obtain the visual clustering results of Chinese aviation network nodes under different K values, as well as silhouette coefficients for evaluating the clustering effect of each indicator in order to obtain the hierarchical classification of aviation networks under different indicators. Finally, the silhouette coefficient is optimal when the K value is 4. Thus, the clustering results of the four layers of the aviation network can be obtained. According to the experimental results, the complex network association discovery method combined with K-means algorithm has better applicability and simplicity, while the accuracy is improved.

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Keywords

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