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Dynamic network analytics for recommending scientific collaborators

Author

Listed:
  • Lu Huang

    (Beijing Institute of Technology)

  • Xiang Chen

    (Beijing Institute of Technology)

  • Yi Zhang

    (University of Technology Sydney)

  • Yihe Zhu

    (New H3C TECHNOLOGY CO LTD)

  • Suyi Li

    (Beijing Institute of Technology)

  • Xingxing Ni

    (Beijing Institute of Technology)

Abstract

Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field.

Suggested Citation

  • Lu Huang & Xiang Chen & Yi Zhang & Yihe Zhu & Suyi Li & Xingxing Ni, 2021. "Dynamic network analytics for recommending scientific collaborators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8789-8814, November.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04164-x
    DOI: 10.1007/s11192-021-04164-x
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    Cited by:

    1. Chi Jiang & Xiao Ma & Jiangfeng Zeng & Yin Zhang & Tingting Yang & Qiumiao Deng, 2023. "TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3453-3471, June.
    2. Xiuxiu Li & Mingyang Wang & Xu Liu, 2024. "Predicting collaborative relationship among scholars by integrating scholars’ content-based and structure-based features," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3225-3244, June.

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