Sensitivity metrics of complex network based on co-occurrence truth table: exemplified by a high-speed rail network

Juanjuan Luo, Teng Fei (), Meng Tian, Yifei Liu and Meng Bian
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Juanjuan Luo: Wuhan University
Teng Fei: Wuhan University
Meng Tian: Yangzhou University
Yifei Liu: Wuhan University
Meng Bian: Wuhan University

Journal of Geographical Systems, 2023, vol. 25, issue 4, No 4, 519-538

Abstract: Abstract As a mathematical scaffold for network science, graph theory abstracts complex systems into complex networks. However, graphs ignore the multiplicity of combinatorial relationships in network systems, leading to limitations in graph-based metrics reflecting the importance of nodes. To address the shortcomings of graphs in describing network complexity, this study proposes the use of co-occurrence pattern truth tables to represent the combinations of multiple nodes in a network. Based on this, the concept of positive sensitivity is proposed to measure one aspect of the importance of nodes in a network. In addition, network sensitivity is proposed to depict the robustness of the network. The proposed approach is verified to be workable with Monte Carlo simulations and a real network exemplified by the high-speed rail network, constructed with provincial capitals of China as nodes. The results in comparison with traditional graph theory-based indices show that both the nodes and the network are assessed with reasonable results different from those of the graph-derived metrics. This study focuses on the combinatorial relationships of nodes in networks, providing a new perspective for the analysis of complex networks.

Keywords: Complex network; Node importance; Network sensitivity; Boolean function; Co-occurrence truth table (search for similar items in EconPapers)
JEL-codes: L92 R41 R42 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10109-023-00419-8

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