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ORIGINAL RESEARCH article

Front. Phys.
Sec. Social Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1492731
This article is part of the Research Topic Network Learning and Propagation Dynamics Analysis View all 13 articles

Exploring Network Dynamics in Scientific Innovation: Collaboration, Knowledge Combination, and Innovative Performance

Provisionally accepted
Yangyang Jia Yangyang Jia 1Hongshu Chen Hongshu Chen 1*Jingkang Liu Jingkang Liu 1Xuefeng Wang Xuefeng Wang 1Rui Guo Rui Guo 2Ximeng Wang Ximeng Wang 3
  • 1 Beijing Institute of Technology, Beijing, China
  • 2 University of Chinese Academy of Sciences, Beijing, Beijing, China
  • 3 Postal Savings Bank of China, Beijing, China

The final, formatted version of the article will be published soon.

    The system of scientific innovation can be characterized as a complex, multi-layered network of actors, their products and knowledge elements. Despite the progress that has been made, a more comprehensive understanding of the interactions and dynamics of this multi-layered network remains a significant challenge. This paper constructs a multilayer longitudinal network to abstract institutions, products and ideas of the scientific system, then identifies patterns and elucidates the mechanism through which actor collaboration and their knowledge transmission influence the innovation performance and network dynamics. Aside from fostering a collaborative network of institutions via co-authorship, fine-grained knowledge elements are extracted using KeyBERT from academic papers to build knowledge network layer. Empirical studies demonstrate that actor collaboration and their unique and diverse ideas have a positive impact on the performance of the research products. This paper also presents empirical evidence that the embeddedness of the actors, their ideas and features of their research products influence the network dynamics. This study gains a deeper understanding of the driving factors that impact the interactions and dynamics of the multi-layered scientific networks.

    Keywords: scientific innovation1, complex network2, network dynamics3, stochastic actororiented model4, collaboration network5, knowledge network6

    Received: 07 Sep 2024; Accepted: 21 Nov 2024.

    Copyright: © 2024 Jia, Chen, Liu, Wang, Guo and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hongshu Chen, Beijing Institute of Technology, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.