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

Jianyu Zhao, Jiang Wei, Lean Yu and Xi Xi

Structural Change and Economic Dynamics, 2022, vol. 63, issue C, 367-382

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.

Keywords: Robustness; Targeted attacks; Knowledge networks; Maximal connected subgraph; Network efficiency (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:streco:v:63:y:2022:i:c:p:367-382

DOI: 10.1016/j.strueco.2022.05.008

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