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Dynamic network analytics for recommending scientific collaborators

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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 However, the existing methods operate on static data, which can render many suggestions less useful or out of date.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.

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Acknowledgements

This work was supported by the National Science Foundation of China Funds [Grant No. 71774013] and the Australian Research Council under Discovery Early Career Researcher Award DE190100994. Our heartfelt appreciation goes to Xiaoli Cao and Changtian Wang for their contributions to this paper.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, P.R. China

    Lu Huang, Xiang Chen, Suyi Li & Xingxing Ni

  2. Centre for Artificial Intelligence, Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW, 2007, Australia

    Yi Zhang

  3. New H3C TECHNOLOGY CO LTD, 466 Changhe Road, Binjiang District, Hangzhou, 310000, P.R. China

    Yihe Zhu

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  1. Lu Huang
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Correspondence to Xiang Chen.

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Huang, L., Chen, X., Zhang, Y. et al. Dynamic network analytics for recommending scientific collaborators. Scientometrics 126, 8789–8814 (2021). https://doi.org/10.1007/s11192-021-04164-x

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