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Robustness of knowledge networks under targeted attacks: Electric vehicle field of China evidence

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  • Zhao, Jianyu
  • Wei, Jiang
  • Yu, Lean
  • Xi, Xi

Abstract

A new framework is proposed to comprehensively explore the robustness (structure and function) of knowledge networks under different targeted attacks. Results show that (1) recalculated-based attacks cause greater damage than initial-based attacks. Meanwhile, a higher price is needed to destroy the structure than disrupt the function of the knowledge network. (2) Maintaining the structure of the knowledge network not only depends on the position that high-degree knowledge elements occupy in the maximal connected subgraph and their upper limit of combinatorial value but also relies on the reducing requirement of new knowledge elements caused by local search and connections. (3) Embedding more knowledge elements that are involved in multiple knowledge domains with medium-degree distribution can consolidate the knowledge network's structure. With regard to enhancing network efficiency, highly relevant knowledge elements create an inverted U-shape influence, while diverse knowledge elements continuously exert positive effects.

Suggested Citation

  • Zhao, Jianyu & Wei, Jiang & Yu, Lean & Xi, Xi, 2022. "Robustness of knowledge networks under targeted attacks: Electric vehicle field of China evidence," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 367-382.
    • Handle: RePEc:eee:streco:v:63:y:2022:i:c:p:367-382
    DOI: 10.1016/j.strueco.2022.05.008
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Jianyu & Yu, Lean & Xi, Xi & Li, Shengliang, 2023. "Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy," Omega, Elsevier, vol. 120(C).

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