IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v63y2022icp367-382.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X22000741
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2022.05.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. Gautam Ahuja, 2000. "The duality of collaboration: inducements and opportunities in the formation of interfirm linkages," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 317-343, March.
    3. Corey C. Phelps & Ralph Heidl & Anu Wadhwa, 2012. "Networks, knowledge, and knowledge networks: A critical review and research agenda," Post-Print hal-00715591, HAL.
    4. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2004. "Error and attack tolerance of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 388-394.
    5. Jaffe, Adam B., 2000. "The U.S. patent system in transition: policy innovation and the innovation process," Research Policy, Elsevier, vol. 29(4-5), pages 531-557, April.
    6. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    7. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    8. Linda Argote & Bill McEvily & Ray Reagans, 2003. "Managing Knowledge in Organizations: An Integrative Framework and Review of Emerging Themes," Management Science, INFORMS, vol. 49(4), pages 571-582, April.
    9. Guan, Jiancheng & Zhang, Jingjing & Yan, Yan, 2015. "The impact of multilevel networks on innovation," Research Policy, Elsevier, vol. 44(3), pages 545-559.
    10. Siskos, Eleftherios & Tsotsolas, Nikos, 2015. "Elicitation of criteria importance weights through the Simos method: A robustness concern," European Journal of Operational Research, Elsevier, vol. 246(2), pages 543-553.
    11. Manuel Trajtenberg & Adam B. Jaffe & Michael S. Fogarty, 2000. "Knowledge Spillovers and Patent Citations: Evidence from a Survey of Inventors," American Economic Review, American Economic Association, vol. 90(2), pages 215-218, May.
    12. Pilkington, Alan & Dyerson, Romano & Tissier, Omid, 2002. "The electric vehicle:: Patent data as indicators of technological development," World Patent Information, Elsevier, vol. 24(1), pages 5-12, March.
    13. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.
    14. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2003. "Efficiency of scale-free networks: error and attack tolerance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 622-642.
    15. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guan, Jiancheng & Yan, Yan & Zhang, Jing Jing, 2017. "The impact of collaboration and knowledge networks on citations," Journal of Informetrics, Elsevier, vol. 11(2), pages 407-422.
    2. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    3. Zakaryan, Arusyak, 2023. "Organizational knowledge networks, search and exploratory invention," Technovation, Elsevier, vol. 122(C).
    4. Wen, Jinyan & Qualls, William J. & Zeng, Deming, 2021. "To explore or exploit: The influence of inter-firm R&D network diversity and structural holes on innovation outcomes," Technovation, Elsevier, vol. 100(C).
    5. Yan Yan & Jiancheng Guan, 2018. "How multiple networks help in creating knowledge: evidence from alternative energy patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 51-77, April.
    6. Zhang, Zhengang & Luo, Taiye, 2020. "Network capital, exploitative and exploratory innovations——from the perspective of network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    7. Taiye Luo & Zhengang Zhang, 2021. "Multi-network embeddedness and innovation performance of R&D employees," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8091-8107, September.
    8. Christoph Grimpe & Katrin Hussinger & Wolfgang Sofka, 2023. "Reaching beyond the acquirer-Target Dyad in M&A – Linkages to External knowledge sources and target firm valuation," DEM Discussion Paper Series 23-01, Department of Economics at the University of Luxembourg.
    9. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    10. Mahmoud Ibrahim Fallatah, 2021. "Innovating in the Desert: a Network Perspective on Knowledge Creation in Developing Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(3), pages 1533-1551, September.
    11. Yue Wang & Ning Li & Bin Zhang & Qian Huang & Jian Wu & Yang Wang, 2023. "The effect of structural holes on producing novel and disruptive research in physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1801-1823, March.
    12. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    13. 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).
    14. Morehead, Raymond & Noore, Afzel, 2007. "Novel hybrid mitigation strategy for improving the resiliency of hierarchical networks subjected to attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 603-612.
    15. Guan, JianCheng & Zhang, JingJing, 2018. "The dynamics of partner and knowledge portfolios in alternative energy field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2869-2879.
    16. Jiao, Hao & Wang, Tang & Yang, Jifeng, 2022. "Team structure and invention impact under high knowledge diversity: An empirical examination of computer workstation industry," Technovation, Elsevier, vol. 114(C).
    17. Liu, Weiwei & Tao, Yuan & Bi, Kexin, 2022. "Capturing information on global knowledge flows from patent transfers: An empirical study using USPTO patents," Research Policy, Elsevier, vol. 51(5).
    18. Kashyap, G. & Ambika, G., 2019. "Link deletion in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 631-643.
    19. Stefano Scarazzati & Lili Wang, 2019. "The effect of collaborations on scientific research output: the case of nanoscience in Chinese regions," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 839-868, November.
    20. Lian, Xiangpeng & Guo, Ying & Su, Jun, 2021. "Technology stocks: A study on the characteristics that help transfer public research to industry," Research Policy, Elsevier, vol. 50(10).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:streco:v:63:y:2022:i:c:p:367-382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.