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Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model

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Abstract

The innovation and development of emerging technology mostly depend on the way of knowledge convergence defined as the blurring of previously distinct domain-specific knowledge. This paper aims to explore the potential motivation of knowledge convergence and find the law of knowledge convergence, taking the solar energy field as an example. We established Keywords co-occurrence networks of solar energy literature in 2008–2017, and then link prediction is introduced to study the structural mechanism of knowledge convergence. We found that: (1) the common neighbor index better characterizes the knowledge convergence pattern in the knowledge networks among four similarity indicators. (2) The keywords co-occurrence network could effectively mine the structural characteristics of knowledge convergence; (3) the convergence cycle of knowledge in the field of solar energy was about 4 years; (4) keywords with higher betweenness centrality or eigenvector centrality easily generated knowledge convergence; (5) a literature knowledge convergence prediction model is proposed based on these results; and (6) the prediction results showed that scholars should pay attention to six basic issues including energy storage, efficiency, cost, ecological effect, application scenarios, and hybrid photovoltaic systems. This work can provide guidance not only for scholars to grasp the research direction and to generate more innovations but for the government to formulate the policies of government funding.

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Availability of data and material

The data of this study is downloaded from Web of Science, which is a public literature database. It can be downloaded by any paying user.

Code availability

In this paper, the experiment on network structure is completed by the software Gephi, and the link prediction model is completed by the custom coding in MATLAB.

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Acknowledgements

The authors would like to express their gratitude to Jianhe Guan, Yixiong Zhang and Kaiming Wang who helped a lot during this work, including processing data and providing analysis suggestions.

Funding

This research is supported by grants from the National Natural Science Foundation of China (Grant No. 42001236, 71991481, 71991480), Beijing Outstanding Talent Training Foundation (Grant No. 2018000020124G151), and the Fundamental Research Funds for the Central Universities (Grant No. 2-9-2018-079).

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Authors and Affiliations

  1. School of Economics and Management, China University of Geosciences, Beijing, 100083, China

    Yueran Duan

  2. School of Information Engineering, China University of Geosciences, Beijing, 100083, China

    Qing Guan

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  1. Yueran Duan
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  2. Qing Guan
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Duan Yueran organized and wrote the paper. Guan Qing provided several suggestions and revised the paper.

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Correspondence to Qing Guan.

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Duan, Y., Guan, Q. Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model. Scientometrics 126, 3749–3773 (2021). https://doi.org/10.1007/s11192-021-03901-6

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