High-Order Community Detection in the Air Transport Industry: A Comparative Analysis among 10 Major International Airlines

Huijuan Yang,Meilong Le

doi:10.3390/app11209378

Abstract

Community detection in a complex network is an ongoing field. While the air transport network has gradually formed as a complex system, the topological and geographical characteristics of airline networks have become crucial in understanding the network dynamics and airports’ roles. This research tackles the highly interconnected parts in weighted codeshare networks. A dataset comprising ten major international airlines is selected to conduct a comparative analysis. The result confirms that the clique percolation method can be used in conjunction with other metrics to shed light on air transport network topology, recognizing patterns of inter- and intra-community connections. Moreover, the topological detection results are interpreted and explained from a transport geographical perspective, with the physical airline network structure. As complex as it may seem, the airline network tends to be a relatively small system with only a few high-order communities, which can be characterized by geographical constraints. This research also contributes to the literature by capturing new insights regarding the topological patterns of the air transport industry. Particularly, it reveals the wide hub-shifting phenomenon and the possibility of airlines with different business models sharing an identical topology profile.

Highlights

Topology describes properties of space that are preserved under continuous deformations, while network topology explores the way components arrange and connect within a system [1]
With the tremendous growth of the complex networks theory and application, the air transport network has gradually formed as a complex system of flights, which considers airports and direct flights as vertices and edges [2]
Unlike the structures identified in low-order communities, the clique community detection results show that most of the codeshare networks consist of three three-clique groups

Summary

Introduction

Topology describes properties of space that are preserved under continuous deformations, while network topology explores the way components arrange and connect within a system [1]. With the tremendous growth of the complex networks theory and application, the air transport network has gradually formed as a complex system of flights, which considers airports and direct flights as vertices and edges [2]. Vertices or nodes with network-specific roles emphasize the determinants of the network topology and performance [3]. Communities usually represent the multiple subgroups or clusters, which consist of groups of vertices that locally, densely interconnect, but sparsely connect to other groups [4,5]. A community in the transportation industry may exist as several cities, which are frequently connected by bus, train, or flights. The existence of communities evidences the hierarchy among the interactions and features within the network

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