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

Author

Listed:
  • Juanjuan Luo

    (Wuhan University)

  • Teng Fei

    (Wuhan University)

  • Meng Tian

    (Yangzhou University)

  • Yifei Liu

    (Wuhan University)

  • Meng Bian

    (Wuhan University)

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.

Suggested Citation

  • Juanjuan Luo & Teng Fei & Meng Tian & Yifei Liu & Meng Bian, 2023. "Sensitivity metrics of complex network based on co-occurrence truth table: exemplified by a high-speed rail network," Journal of Geographical Systems, Springer, vol. 25(4), pages 519-538, October.
    • Handle: RePEc:kap:jgeosy:v:25:y:2023:i:4:d:10.1007_s10109-023-00419-8
    DOI: 10.1007/s10109-023-00419-8
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    3. Georgiana Madar & Hanna Maoh & William Anderson, 2020. "Examining the robustness of the Ontario truck road network," Journal of Geographical Systems, Springer, vol. 22(3), pages 309-333, July.
    4. A. Hernando & D. Villuendas & C. Vesperinas & M. Abad & A. Plastino, 2010. "Unravelling the size distribution of social groups with information theory in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 76(1), pages 87-97, July.
    5. Wang, Jianwei & Rong, Lili & Zhang, Liang & Zhang, Zhongzhi, 2008. "Attack vulnerability of scale-free networks due to cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6671-6678.
    6. Zengwang Xu & Daniel Sui, 2007. "Small-world characteristics on transportation networks: a perspective from network autocorrelation," Journal of Geographical Systems, Springer, vol. 9(2), pages 189-205, June.
    7. Alan Murray & Timothy Matisziw & Tony Grubesic, 2007. "Critical network infrastructure analysis: interdiction and system flow," Journal of Geographical Systems, Springer, vol. 9(2), pages 103-117, June.
    8. Lucia Cavallaro & Annamaria Ficara & Pasquale De Meo & Giacomo Fiumara & Salvatore Catanese & Ovidiu Bagdasar & Wei Song & Antonio Liotta, 2020. "Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-22, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Complex network; Node importance; Network sensitivity; Boolean function; Co-occurrence truth table;
    All these keywords.

    JEL classification:

    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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