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A layered framework to study collaboration as a form of knowledge sharing and diffusion

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  • Liu, Yuxian
  • Rousseau, Ronald
  • Guns, Raf

Abstract

Collaboration can be described using layered systems such as the article–author–institute–country structure. These structures can be considered ‘cascades’ or ‘chains’ of bipartite networks. We introduce a framework for characterizing and studying the intensity of collaboration between entities at a given level (e.g., between institutions). Specifically, we define the notions of significant, essential and vital nodes, and significant, essential and vital sub paths to describe the spread of knowledge through collaboration in such systems. Based on these notions, we introduce relative and absolute proper essential node (PEN) centrality as indicators of a node's importance for diffusion of knowledge through collaboration.

Suggested Citation

  • Liu, Yuxian & Rousseau, Ronald & Guns, Raf, 2013. "A layered framework to study collaboration as a form of knowledge sharing and diffusion," Journal of Informetrics, Elsevier, vol. 7(3), pages 651-664.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:3:p:651-664
    DOI: 10.1016/j.joi.2013.04.002
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    References listed on IDEAS

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    1. Hildrun Kretschmer & Ronald Rousseau, 2001. "Author inflation leads to a breakdown of Lotka's law," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(8), pages 610-614.
    2. Rousseau, Ronald & Liu, Yuxian & Ye, Fred Y., 2012. "A preliminary investigation on diffusion through a layered system," Journal of Informetrics, Elsevier, vol. 6(2), pages 177-191.
    3. Blaise Cronin, 2001. "Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(7), pages 558-569.
    4. Yuxian Liu & Ronald Rousseau, 2010. "Knowledge diffusion through publications and citations: A case study using ESI-fields as unit of diffusion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(2), pages 340-351, February.
    5. Yuxian Liu & Ronald Rousseau, 2010. "Knowledge diffusion through publications and citations: A case study using ESI‐fields as unit of diffusion," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(2), pages 340-351, February.
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    Citations

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    Cited by:

    1. Eustache Mêgnigbêto, 2018. "Correlation Between Transmission Power and Some Indicators Used to Measure the Knowledge-Based Economy: Case of Six OECD Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(4), pages 1168-1183, December.
    2. Chi, Yuxue & Tang, Xianyi & Liu, Yijun, 2022. "Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Vivek Kumar Singh & Ashraf Uddin & David Pinto, 2015. "Computer science research: the top 100 institutions in India and in the world," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 529-553, August.
    4. Meijun Liu & Dongbo Shi & Jiang Li, 2017. "Double-edged sword of interdisciplinary knowledge flow from hard sciences to humanities and social sciences: Evidence from China," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-16, September.
    5. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    6. Wen-Yau Cathy Lin, 2021. "Effects of open access and articles-in-press mechanisms on publishing lag and first-citation speed: a case on energy and fuels journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4841-4869, June.
    7. Xiaoling Sun & Hongfei Lin & Kan Xu & Kun Ding, 2015. "How we collaborate: characterizing, modeling and predicting scientific collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 43-60, July.
    8. Le Song & Guilong Zhu & Xiao Yin, 2024. "Evaluating the wisdom of scholar crowds from the perspective of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5103-5139, September.
    9. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    10. Rousseau, Ronald & Liu, Yuxian & Guns, Raf, 2013. "Mathematical properties of Q-measures," Journal of Informetrics, Elsevier, vol. 7(3), pages 737-745.
    11. Matteo Cinelli & Giovanna Ferraro & Antonio Iovanella, 2022. "Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3959-3976, July.
    12. Goldman, Alyssa W., 2014. "Conceptualizing the interdisciplinary diffusion and evolution of emerging fields: The case of systems biology," Journal of Informetrics, Elsevier, vol. 8(1), pages 43-58.

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