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Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data

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Abstract

Understanding the complex patterns in research funding plays a fundamental role in comprehensively revealing funding preferences and informing ideas for future strategic innovation. This is especially true when the funding policies need to be constantly shifted to accommodate highly complex and ever-changing demands for technological, economic, and social development. To this end, we investigate the associations between funding agencies and the topics they fund in an attempt to understand funding patterns at both an organizational level and a topic level. In this paper, the links between heterogeneous nodes, organizations and topics, are mapped to a two-mode organization–topic network. The collaborative interactions formed by funding organizations and the semantic networks constituted by word embedding-enhanced topics are revealed and analyzed simultaneously. The methodology is demonstrated through a case study on big data research involving 9882 articles from the Web of Science over the period 2010 to 2019. The result shows a comprehensive picture of the topics that governments, academic institutions, and industrial funding organizations prefer to fund, which provide potential decision support for agencies and organizations who are exploring funding patterns, estimating funding trends, and updating their funding strategies.

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Notes

  1. The search strategy for funding organization: FO = (a* or b* or c* or d* or e* or f* or g* or h* or i* or j* or k* or l* or m* or n* or o* or p* or q* or r* or s* or t* or u* or v* or w* or x* or y* or z* or 1* or 2* or 3* or 4* or 5* or 6* or 7* or 8* or 9* or 0*).

  2. Complete and detailed funding proportion for all topics are provided in “Appendix”.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 72004009, 61872033, and 71804016, and also supported by Beijing Institute of Technology Research Fund Program for Young Scholars Beijing and Nova Program (Z201100006820015) from the Beijing Municipal Science and Technology Commission.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Qianqian Jin & Hongshu Chen

  2. Cyber Finance Department, Postal Savings Bank of China, Beijing, China

    Ximeng Wang

  3. School of Logistics, Beijing Wuzi University, Beijing, China

    Tingting Ma

  4. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

    Fei Xiong

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  1. Qianqian Jin
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  2. Hongshu Chen
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  3. Ximeng Wang
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  5. Fei Xiong
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Correspondence to Hongshu Chen.

Appendix

Appendix

See Table 3.

Table 3 Breakdown of GA&I funding topics have received over the years
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Jin, Q., Chen, H., Wang, X. et al. Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data. Scientometrics 127, 5415–5440 (2022). https://doi.org/10.1007/s11192-021-04253-x

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