A study on centrality measures in weighted networks: A case of the aviation network

Shuying Zhao; Shaowei Sun; Shuying Zhao; Shaowei Sun

doi:10.3934/math.2024178

AIMS Mathematics

2024, Volume 9, Issue 2: 3630-3645. doi: 10.3934/math.2024178

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Research article Special Issues

A study on centrality measures in weighted networks: A case of the aviation network

Shuying Zhao ,
Shaowei Sun ^,

School of Science, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, 310023, China

Received: 10 November 2023 Revised: 03 December 2023 Accepted: 12 December 2023 Published: 09 January 2024
MSC : 05C82

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of the topological structure of the network, the paper recognized critical cities in this network, then simulation experiments were conducted on key city nodes from the perspectives of network dynamics and robustness. The results indicated that, compared with other methods, weighted Laplacian energy centrality can identify the city nodes with the most spreading influence in the network. From the perspective of network robustness, the identified key nodes also have the characteristics of accurately and quickly destroying network robustness.
- weighted network,
- Laplacian matrix,
- identification of key nodes,
- SIR model,
- robustness
Citation: Shuying Zhao, Shaowei Sun. A study on centrality measures in weighted networks: A case of the aviation network[J]. AIMS Mathematics, 2024, 9(2): 3630-3645. doi: 10.3934/math.2024178

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Abstract

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of the topological structure of the network, the paper recognized critical cities in this network, then simulation experiments were conducted on key city nodes from the perspectives of network dynamics and robustness. The results indicated that, compared with other methods, weighted Laplacian energy centrality can identify the city nodes with the most spreading influence in the network. From the perspective of network robustness, the identified key nodes also have the characteristics of accurately and quickly destroying network robustness.

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